Eyeglasses to detect abnormal medical events including stroke and migraine

ABSTRACT

System and Method to detect an abnormal medical event based on an asymmetrical change to blood flow. The system includes right-side and left-side head-mounted devices to measure signals indicative of photoplethysmographic signals (PPG signals) on the right and left sides of a user&#39;s head, and a computer that detects the abnormal medical event based on an asymmetrical change to blood flow recognizable in the PPG signals. Optionally, the asymmetrical change to the blood flow corresponds to a deviation of the PPG signals compared to a baseline that is based on previous measurements of PPG signals of the user, taken before the abnormal medical event.

CROSS-REFERENCE TO RELATED APPLICATIONS

This Application claims priority to U.S. Provisional Patent ApplicationNo. 62/722,655, filed Aug. 24, 2018, and U.S. Provisional PatentApplication No. 62/874,430, filed Jul. 15, 2019.

This Application is a Continuation-In-Part of U.S. application Ser. No.16/453,993, filed Jun. 26, 2019. U.S. Ser. No. 16/453,993 is aContinuation-In-Part of U.S. application Ser. No. 16/375,841, filed Apr.4, 2019. U.S. Ser. No. 16/375,841 is a Continuation-In-Part of U.S.application Ser. No. 16/156,493, filed Oct. 10, 2018. U.S. Ser. No.16/156,493, is a Continuation-In-Part of U.S. application Ser. No.15/635,178, filed Jun. 27, 2017, now U.S. Pat. No. 10,136,856, whichclaims priority to U.S. Provisional Patent Application No. 62/354,833,filed Jun. 27, 2016, and U.S. Provisional Patent Application No.62/372,063, filed Aug. 8, 2016.

U.S. Ser. No. 16/156,493 is also a Continuation-In-Part of U.S.application Ser. No. 15/231,276, filed Aug. 8, 2016, which claimspriority to U.S. Provisional Patent Application No. 62/202,808, filedAug. 8, 2015, and U.S. Provisional Patent Application No. 62/236,868,filed Oct. 3, 2015.

U.S. Ser. No. 16/156,493 is also a Continuation-In-Part of U.S.application Ser. No. 15/832,855, filed Dec. 6, 2017, now U.S. Pat. No.10,130,308, which claims priority to U.S. Provisional Patent ApplicationNo. 62/456,105, filed Feb. 7, 2017, and U.S. Provisional PatentApplication No. 62/480,496, filed Apr. 2, 2017, and U.S. ProvisionalPatent Application No. 62/566,572, filed Oct. 2, 2017. U.S. Ser. No.15/832,855 is a Continuation-In-Part of U.S. application Ser. No.15/182,592, filed Jun. 14, 2016, now U.S. Pat. No. 10,165,949, aContinuation-In-Part of U.S. application Ser. No. 15/231,276, filed Aug.8, 2016, a Continuation-In-Part of U.S. application Ser. No. 15/284,528,filed Oct. 3, 2016, now U.S. Pat. No. 10,113,913, a Continuation-In-Partof U.S. application Ser. No. 15/635,178, filed Jun. 27, 2017, now U.S.Pat. No. 10,136,856, and a Continuation-In-Part of U.S. application Ser.No. 15/722,434, filed Oct. 2, 2017.

U.S. Ser. No. 15/832,855 is a Continuation-In-Part of U.S. applicationSer. No. 15/182,566, filed Jun. 14, 2016, now U.S. Pat. No. 9,867,546,which claims priority to U.S. Provisional Patent Application No.62/175,319, filed Jun. 14, 2015, and U.S. Provisional Patent ApplicationNo. 62/202,808, filed Aug. 8, 2015.

U.S. Ser. No. 15/182,592 claims priority to U.S. Provisional PatentApplication No. 62/175,319, filed Jun. 14, 2015, and U.S. ProvisionalPatent Application No. 62/202,808, filed Aug. 8, 2015.

U.S. Ser. No. 15/284,528 claims priority to U.S. Provisional PatentApplication No. 62/236,868, filed Oct. 3, 2015, and U.S. ProvisionalPatent Application No. 62/354,833, filed Jun. 27, 2016, and U.S.Provisional Patent Application No. 62/372,063, filed Aug. 8, 2016.

U.S. Ser. No. 16/156,493 is also a Continuation-In-Part of U.S.application Ser. No. 15/833,115, filed Dec. 6, 2017, now U.S. Pat. No.10,130,261. U.S. Ser. No. 15/833,115 is a Continuation-In-Part of U.S.application Ser. No. 15/182,592, a Continuation-In-Part of U.S.application Ser. No. 15/231,276, filed Aug. 8, 2016, aContinuation-In-Part of U.S. application Ser. No. 15/284,528, aContinuation-In-Part of U.S. application Ser. No. 15/635,178, and aContinuation-In-Part of U.S. application Ser. No. 15/722,434, filed Oct.2, 2017.

U.S. Ser. No. 16/453,993 is also a Continuation-In-Part of U.S.application Ser. No. 16/147,695, filed Sep. 29, 2018. U.S. Ser. No.16/147,695 is a Continuation of U.S. application Ser. No. 15/182,592,filed Jun. 14, 2016, which claims priority to U.S. Provisional PatentApplication No. 62/175,319, filed Jun. 14, 2015, and U.S. ProvisionalPatent Application No. 62/202,808, filed Aug. 8, 2015.

ACKNOWLEDGMENTS

Gil Thieberger would like to thank his holy and beloved teacher, LamaDvora-hla, for her extraordinary teachings and manifestation of wisdom,love, compassion and morality, and for her endless efforts, support, andskills in guiding him and others on their paths to freedom and ultimatehappiness. Gil would also like to thank his beloved parents for raisinghim with love and care.

BACKGROUND

There are many medical illnesses that can benefit greatly from earlyintervention. For example in ischemic stroke, time reduction from onsetof the initial event to administration of antithrombotic medication orinterventional radiology can leave an individual with greatly enhancedneurologic function. Similarly, early treatment of migraine or infectioncan lead to reduced convalescence time, enhanced well-being andincreased productivity.

The need for early intervention necessitates early diagnosis of themedical condition. Despite advances in wearables, currently the vastmajority of individuals do not have access to technology that canmeasure significant physiologic changes in real time and help withdiagnosis of migraine, headache, stroke, infection and other significantmedical conditions. As a result, many people encounter delays indiagnosis and treatment, which results in increased morbidity andmortality. Existing wearables often offer limited sensors, poor signals,and suffer from significant artifacts.

SUMMARY

Some embodiments described herein utilize head-mounted sensors to obtainmultiple photoplethysmogram signals at various regions on a user's head.A photoplethysmogram signal (PPG signal) is an optically obtainedplethysmogram that is indicative of blood volume changes in themicrovascular bed of tissue. A PPG signal is often obtained by using apulse oximeter, which illuminates the skin and measures changes in lightabsorption. Another possibility for obtaining a PPG signal is using animaging photoplethysmography (iPPG) device. As opposed to typical PPGdevices, which usually come in contact with the skin, iPPG usually doesnot require contact with the skin and is obtained by a non-contactsensor, such as a video camera. Other terms that may be used to refer toiPPG include multi-site photoplethysmography (MPPG) or remotephotoplethysmography (rPPG).

The multiple PPG signals can be indicative of blood flow and/or changesthereto, which may be indicative of an onset and/or occurrence of anabnormal medical event. Some examples of medical events that areconsidered an “abnormal medical event” herein include an Ischemicstroke, a migraine, a headache, cellulitis (soft tissue infection),dermatitis (skin infection), and an ear infection.

One aspect of this disclosure includes a system configured to detect anabnormal medical event. The system includes at least one right-sidehead-mounted device configured to measure at least two signalsindicative of photoplethysmographic signals (PPG_(SR1) and PPG_(SR2),respectively) at first and second regions of interest (ROI_(R1) andROI_(R2), respectively) on the right side of a user's head, whereROI_(R1) and ROI_(R2) are located at least 2 cm apart. The system alsoincludes at least one left-side head-mounted device configured tomeasure at least two signals indicative of photoplethysmographic signals(PPG_(SL1) and PPG_(SL2), respectively) at first and second regions ofinterest (ROI_(L1) and ROI_(L2), respectively) on the left side of theuser's head, where ROI_(L1) and ROI_(L2) are located at least 2 cmapart. The system also includes a computer configured to detect theabnormal medical event based on an asymmetrical change to blood flowrecognizable in PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2).Optionally, the asymmetrical change to the blood flow corresponds to adeviation of PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2) compared toa baseline that is based on previous measurements of PPG_(SR1),PPG_(SR2), PPG_(SL1), and PPG_(SL2) of the user, taken before theabnormal medical event. Optionally, the computer utilizes a machinelearning based approach in which it is configured to generate featurevalues based on data that includes: (i) PPG_(SR1), PPG_(SR2), PPG_(SL1),and PPG_(SL2) of the user, and (ii) the previous measurements ofPPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2) of the user. The computerthen utilizes a model to calculate, based on the feature values, a valueindicative of whether the user is experiencing the abnormal medicalevent.

Having the computer base its detection of the abnormal medical event onmultiple PPG signals (at least four in some embodiments describedherein), may confer several advantages. In particular, having the PPGsignals measured on different sides of the head can help identify casesin which changes in blood flow are localized to a certain body region(e.g., a certain side of the head). Identifying such occurrences is notpossible with a single PPG signal, and/or with PPG signals that aremeasured on a single side of the head. Having a single PPG signal, ormultiple PPG signals from the same side, does not provide comparativeinformation that can help identify that the change to blood flow isasymmetrical (i.e., not the same on both sides of the head), sincemeasurements from both sides of the head are needed to reach such aconclusion. Thus, systems that rely on a single PPG signal, or multiplePPG signals from the same side of the head, cannot provide the datarequired in order to detect abnormal medical event characterized by anasymmetrical change to blood flow, as embodiments described herein arecapable of detecting.

BRIEF DESCRIPTION OF THE DRAWINGS

The embodiments are herein described by way of example only, withreference to the following drawings:

FIG. 1a illustrates smartglasses which include contactphotoplethysmographic devices;

FIG. 1b illustrates smartglasses that include first and secondinward-facing cameras;

FIG. 2 illustrates smartglasses that include four inward-facing cameras;

FIG. 3 illustrates an embodiment of a system that includes a singlehead-mounted thermal camera that may be used to detect a stroke;

FIG. 4 illustrates a scenario in which an alert regarding a possiblestroke is issued;

FIG. 5, FIG. 6, FIG. 7, and FIG. 8 illustrate physiological andbehavioral changes that may occur following a stroke;

FIG. 9, FIG. 10, FIG. 11, and FIG. 12 illustrates various activitiesthat a person may be requested to perform in order to determine whetherthe person has suffered from a stroke;

FIG. 13 and FIG. 14 illustrate the difference between a timelyintervention in the event of a stroke and intervention that comes toolate;

FIG. 15a illustrates one embodiment of a system that includes multiplepairs of right and left cameras and locations on the face that they maybe used to measure;

FIG. 15b illustrates a stroke sign that involves decreased blood flow inthe forehead;

FIG. 15c illustrates a stroke sign that involves decreased blood flow ina periorbital region;

FIG. 16a and FIG. 16b illustrate embodiments of a system with ahead-mounted camera located behind the ear;

FIG. 16c illustrates an ischemic stroke that restricts the blood flow tothe side of the head, which may be detected by embodiments describedherein;

FIG. 17a and FIG. 17b illustrate various inward-facing head-mountedcameras coupled to an eyeglasses frame;

FIG. 18 illustrates inward-facing head-mounted cameras coupled to anaugmented reality device;

FIG. 19 illustrates head-mounted cameras coupled to a virtual realitydevice;

FIG. 20 illustrates a side view of head-mounted cameras coupled to anaugmented reality device;

FIG. 21 illustrates a side view of head-mounted cameras coupled to asunglasses frame;

FIG. 22, FIG. 23, FIG. 24, and FIG. 25 illustrate head-mounted systems(HMSs) configured to measure various ROIs relevant to some of theembodiments describes herein;

FIG. 26, FIG. 27, FIG. 28, and FIG. 29 illustrate various embodiments ofsystems that include inward-facing head-mounted cameras havingmulti-pixel sensors (FPA sensors);

FIG. 30a , FIG. 30b , and FIG. 30c illustrate embodiments of two rightand left clip-on devices that are configured to be attached/detachedfrom an eyeglasses frame;

FIG. 31a and FIG. 31b illustrate an embodiment of a clip-on device thatincludes inward-facing head-mounted cameras pointed at the lower part ofthe face and the forehead;

FIG. 32a and FIG. 32b illustrate an embodiment of a single-unit clip-ondevice that is configured to be attached behind an eyeglasses frame;

FIG. 33a and FIG. 33b illustrate embodiments of right and left clip-ondevices that are configured to be attached behind an eyeglasses frame;

FIG. 34 illustrates embodiments of right and left clip-on devices, whichare configured to be attached/detached from an eyeglasses frame, andhave protruding arms to hold inward-facing head-mounted cameras;

FIG. 35a is a schematic illustration of an inward-facing head-mountedcamera embedded in an eyeglasses frame, which utilizes the Scheimpflugprinciple;

FIG. 35b is a schematic illustration of a camera that is able to changethe relative tilt between its lens and sensor planes according to theScheimpflug principle; and

FIG. 36a and FIG. 36b are schematic illustrations of possibleembodiments for computers.

DETAILED DESCRIPTION

In some embodiments, a system configured to detect an abnormal medicalevent includes a computer and several head-mounted devices that are usedto measure photoplethysmographic signals (PPG signals) indicative ofblood flow at various regions on a user's head. Optionally, the systemmay include additional components, such as additional sensors that maybe used to measure the user and/or the environment. Additionally oralternatively, the system may include a frame configured to be worn onthe user's head (e.g., a frame of eyeglasses or smartglasses) and towhich at least some of the head-mounted devices and sensors arephysically coupled. Having sensors, such as the devices that are used tomeasure photoplethysmographic signals, physically coupled to the framemay convey certain advantages, such as having the sensors remain at thesame positions with respect to the head, even when the user's head makesangular movements. Some examples of abnormal medical events that may bedetected by embodiments described herein include an ischemic stroke, amigraine, a headache, cellulitis (soft tissue infection), dermatitis(skin infection), and an ear infection.

In one embodiment, the system includes at least one right-sidehead-mounted device, configured to measure at least two signalsindicative of photoplethysmographic signals (PPG_(SR1) and PPG_(SR2),respectively) at first and second regions of interest (ROI_(R1) andROI_(R2), respectively) on the right side of a user's head. Optionally,ROI_(R1) and ROI_(R2) are located at least 2 cm apart (where cm denotescentimeters). Optionally, each device, from among the at least oneright-side head-mounted device(s), is located to the right of thevertical symmetry axis that divides the user's face.

Additionally, the system includes at least one left-side head-mounteddevice configured to measure at least two signals indicative ofphotoplethysmographic signals (PPG_(SL1) and PPG_(SL2), respectively) atfirst and second regions of interest (ROI_(L1) and ROI_(L2),respectively) on the left side of the user's head. Optionally, eachdevice, from among the at least one right-side head-mounted device(s),is located to the left of the vertical symmetry axis that divides theuser's face.

In some embodiments, ROI_(R1) and ROI_(L1) may be symmetrical regions onthe right and left sides of the head, respectively (with respect to asymmetry axis that splits the face to right and left sides).Additionally or alternatively, ROI_(R2) and ROI_(L2) may be symmetricalregions on the right and left sides of the head, respectively. In otherembodiments, ROI_(R1) and ROI_(L1) may not be symmetrical regions on theright and left side of the head, and/or ROI_(R2) and ROI_(L2) may not besymmetrical regions on the right and left side of the head. Optionally,two regions are considered to be in symmetrical locations if one regionis within 1 cm of the symmetrical region on the head.

Various types of devices may be utilized in order to obtain PPG_(SR1),PPG_(SR2), PPG_(SL1), and/or PPG_(SL2). In one embodiment, the at leastone right-side head-mounted device includes first and second contactphotoplethysmographic devices (PPG₁, PPG₂, respectively). Additionallyor alternatively, the at least one left-side head-mounted device mayinclude third and fourth contact photoplethysmographic devices (PPG₃,PPG₄, respectively). Herein, a “contact photoplethysmographic device” isa photoplethysmographic device that comes in contact with the user'sskin, and typically occludes the area being measured. An example of acontact photoplethysmographic device is the well-known pulse oximeter.

In one example, PPG₁, PPG₂, PPG₃, and PPG₄ are physically coupled to aneyeglasses frame, PPG₁ and PPG₃ are in contact with the nose, and PPG₂and PPG₄ are in contact with regions in the vicinities of the ears(e.g., within a distance of less than 5 cm from the center of theconcha), respectively.

FIG. 1a illustrates smartglasses that include contactphotoplethysmographic devices that may be used to obtain PPG signals, asdescribed above. The contact PPG devices are coupled to a frame 674. Thecontact PPG devices may be coupled at various locations on the frame 674and thus may come in contact with various regions on the user's head.For example, contact PPG device 671 a is located on the right templetip, which brings it to contact with a region behind the user's ear(when the user wears the smartglasses). Contact PPG device 671 b islocated on the right temple of the frame 674, which puts it in contactwith a region on the user's right temple (when wearing thesmartglasses). It is to be noted that in some embodiments, in order tobring the contact PPG device close such that it touches the skin,various apparatuses may be utilized, such as spacers (e.g., made fromrubber or plastic), and/or adjustable inserts that can help bridge apossible gap between a frame's temple and the user's face. Such anapparatus is spacer 672 which brings contact PPG device 671 b in contactwith the user's temple when the user wears the smartglasses.

Another possible location for a contact PPG device is the nose bridge,as contact PPG device 671 c is illustrated in the figure. It is to benoted the contact PPG device 671 c may be embedded in the nose bridge(or one of its components), and/or physically coupled to a part of thenose bridge. The figure also illustrates computer 673, which may beutilized in some embodiments, to perform processing of PPG signalsand/or detection of the abnormal medical event, as described furtherbelow.

It is to be noted that FIG. 1a illustrates but a few of the possiblelocations for contact PPG devices on a frame. Any pair of contact PPGdevices 671 a, 671 b, and 671 c may be the aforementioned PPG₁ and PPG₂.Additionally, some embodiments may include additional contact PPGdevices on each side of the frames. Furthermore, it is to be noted thatwhile contact PPG devices on the left side were not illustrated in thefigure, additional PPG devices may be located in similar locations tothe ones of PPG devices 671 a to 671 c are located, but on the left sideof the frame.

In another embodiment, one or more video cameras may be utilized toobtain PPG_(SR1), PPG_(SR2), PPG_(SL1), and/or PPG_(SL2) utilizingimaging photoplethysmography. Using video cameras can be advantageous insome scenarios, such as stroke, where it is unknown in advanced wherethe physiological response associated with the abnormal medical eventwill appear on the user's head. Thus, in some embodiments involvingthese scenarios, using video cameras may provide a great advantage overcontact PPG devices, because the video cameras cover larger areas, whichincrease the chance to capture on time the physiological responseassociated with the abnormal medical event.

In one embodiment, the at least one right-side head-mounted deviceincludes a first inward-facing camera located more than 0.5 cm away fromROI_(R1) and ROI_(R2), and PPG_(SR1) and PPG_(SR2) are recognizable fromcolor changes in regions in images taken by the first camera.Additionally or alternatively, the at least one left-side head-mounteddevice may include a second inward-facing camera located more than 0.5cm away from ROI_(L1) and ROI_(L2), and PPG_(SL) and PPG_(SL2) arerecognizable from color changes in regions in images taken by the secondcamera. In one embodiment, the system includes both at least one contactPPG device and at least one video camera.

In one embodiment, each of the first and second inward-facing camerasutilizes a sensor having more than 30 pixels, and each of ROI_(R1) andROI_(L1) covers a skin area greater than 6 cm{circumflex over ( )}2,which is illuminated by ambient light. In another embodiment, each ofthe first and second inward-facing cameras utilizes a sensor having morethan 20 pixels, and each of ROI_(R1) and ROI_(L1) covers a skin areagreater than 2 cm{circumflex over ( )}2, which is illuminated by ambientlight. In still another embodiment, each of the first and secondinward-facing cameras utilizes a sensor comprising at least 3×3 pixelsconfigured to detect electromagnetic radiation having wavelengths in atleast a portion of the range of 200 nm to 1200 nm. Optionally, thesystem includes first and second active light sources configured toilluminate portions of the right side of the face (which includeROI_(R1) and ROI_(R2)) and portions of the left side of the face (whichinclude ROI_(L1) and ROI_(L2)), respectively. In one example, the firstand second active light sources are head-mounted light sourcesconfigured to illuminate their respective portions of the face withelectromagnetic radiation having wavelengths in at least a portion ofthe range of 750 nm to 1200 nm.

In one embodiment, due to the angle between the optical axis of acertain inward-facing camera (from among the first and secondinward-facing cameras) and its ROI, the Scheimpflug principle may beemployed in order to capture sharper images with the certaininward-facing camera. For example, when the user wears a frame to whichthe certain inward-facing camera is coupled, the certain inward-facingcamera may have a certain tilt greater than 20 between its sensor andlens planes, in order to capture the sharper images.

FIG. 1b illustrates smartglasses that include first and secondinward-facing cameras, such as the ones described above. The figureillustrates a frame 677 to which a first inward-facing camera 675 a iscoupled above the lens that is in front of the right eye, and a secondinward-facing camera 675 b that is coupled to the frame 677 above thelens that is in front of the left eye. The figure also illustrates acomputer 676 that is coupled to the frame 677, and may be utilized toprocess images obtained by the first inward-facing camera 675 a and/orthe second inward-facing camera 675 b, and/or perform the detection ofthe abnormal medical event based on PPG signals recognizable in imagescaptured by those cameras.

Various regions on the face may be measured in embodiments that utilizeimaging photoplethysmography. In one example, ROI_(R1) and ROI_(L1) arelocated on the user's right and left cheeks, respectively. In anotherexample, ROI_(R2) and ROI_(L2) are located on the right and left sidesof the user's nose, respectively. In yet another example, ROI_(R1) andROI_(L1) are located on the right and left sides of the user's forehead,respectively. In still another example, ROI_(R2) and ROI_(L2) arelocated on the user's right and left temples, respectively.

A configuration consistent with some of the examples described above isillustrated in FIG. 2. In this figure, four inward-facing cameras arecoupled to a frame 540 worn on a user's head: (i) Inward-facing camera544 a is coupled to the top-left side of the frame and captures imagesof ROI 545 a, which is on the left side of the user's forehead; (ii)Inward-facing camera 544 b is coupled to the top-right side of the frameand captures images of ROI 545 b, which is on the right side of theuser's forehead; (iii) Inward-facing camera 546 a is coupled to thebottom-left side of the frame and captures images of ROI 547 a, which ison the user's left cheek; And (iv) Inward-facing camera 546 b is coupledto the bottom-right side of the frame and captures images of ROI 547 b,which is on the user's right cheek.

Herein, sentences of the form “a PPG signal is recognizable from colorchanges in a region in images” refer to effects of blood volume changesdue to pulse waves that may be extracted from a series of images of theregion. These changes may be identified and/or utilized by a computer(e.g., in order to generate a signal indicative of the blood volume atthe region), but need not necessarily be recognizable to the naked eye(e.g., because of their subtlety, the short duration in which theyoccur, or involvement of light outside of the visible spectrum). Forexample, blood flow may cause facial skin color changes (FSCC) thatcorresponds to different concentrations of oxidized hemoglobin due tovarying volume of blood at a certain region due to different stages of acardiac pulse, and/or the different magnitudes of cardiac output.Similar blood flow dependent effects may be viewed with other types ofsignals (e.g., slight changes in cutaneous temperatures due to the flowof blood).

In some embodiments, the system configured to detect the abnormalmedical event may further include first and second outward-facinghead-mounted cameras for taking images of the environment to the rightand to the left of the user's head, respectively. Images taken by thesecameras, which are indicative of illumination towards the face, may beutilized to improve the accuracy of detecting the abnormal medicalevent.

In one example, the first and second outward-facing head-mounted camerasmay be thermal cameras that take thermal measurements of theenvironment. Heat from the environment may affect the surface bloodflow, and thus reduce the accuracy of detecting the abnormal medicalevent. By taking the thermal measurements of the environment intoaccount, the computer is able to detect, and maybe even compensate, fortemperature interferences from the environment. Examples ofoutward-facing head-mounted thermal cameras include thermopile-basedand/or microbolometer-based cameras having one or more pixels.

In another example, the first and second outward-facing head-mountedcameras may be visible-light cameras (such as CMOS cameras), and/orlight intensity sensors (such as photodiodes, photoresistors, and/orphototransistor). Illumination from the environment may affect thesurface blood flow (especially when heating the skin), and/or interferewith the photoplethysmographic signals to be measured, and thus reducethe accuracy of detecting the abnormal medical event. By taking theillumination from the environment into account, the computer is able todetect, and maybe even compensate, for the interferences from theenvironment.

In one embodiment, the at least one right-side head-mounted deviceand/or the at least one left-side head-mounted device are coupled to aclip-on, and the clip-on comprises a body configured to be attached anddetached, multiple times, from a frame configured to be worn on theuser's head. FIG. 30a to FIG. 34 illustrate various examples ofembodiments of systems that include a clip-on which may have theaforementioned head-mounted devices coupled thereto.

Various embodiments described herein include a computer configured todetect the abnormal medical event based on an asymmetrical change toblood flow recognizable in at least PPG_(SR1), PPG_(SR2), PPG_(SL1), andPPG_(SL2). Optionally, the asymmetrical change to the blood flowcorresponds to a deviation of PPG_(SR1), PPG_(SR2), PPG_(SL1), andPPG_(SL2) compared to a baseline based on previous measurements ofPPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2) of the user, taken beforethe abnormal medical event (e.g., minutes, hours, and even days beforethe abnormal medical event). In one example, “a baseline based on theprevious measurements” is one or more values that are calculated basedon the previous measurements (e.g., one or more values representing anormal, baseline blood flow of the user). In another example, “abaseline based on the previous measurements” may be some, or even all,the previous measurements themselves, which may be provided as an inputused in calculations involved in the detection of the abnormal medicalevent (without necessarily calculating an explicit value that isconsidered a “baseline” value of the user's blood flow).

Examples of computers that may be utilized to perform calculationsinvolved in the detection of the abnormal medical event are computersmodeled according to computer 400 or computer 410 illustrated in FIG.36a and FIG. 36b , respectively. Additional examples are the computers673 and 676 illustrated in FIG. 1a and FIG. 1b , respectively. It is tobe noted that the use of the singular term “computer” is intended toimply one or more computers, which jointly perform the functionsattributed to “the computer” herein. In particular, in some embodiments,some functions attributed to the computer, such as preprocessingPPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2), may be performed by aprocessor on a wearable device (e.g., smartglasses) and/or a computingdevice of the user (e.g., smartphone), while other functions, such asthe analysis of sensor data and determining whether the user isexperiencing the abnormal medical event, may be performed on a remoteprocessor, such as a cloud-based server. In other embodiments,essentially all functions attributed to the computer herein may beperformed by a processor on a wearable device (e.g., smartglasses towhich the head-mounted devices are coupled) and/or some other devicecarried by the user, such as a smartwatch or smartphone.

Herein, detecting the abnormal medical event may mean detecting that theuser is suffering from the abnormal medical event, and/or that there isan onset of the abnormal medical event. Additionally, an “abnormal”medical event may be a medical event that the user has yet toexperience, or does not experience most of the time.

In some embodiments, detecting the abnormal medical event may involvecalculating one or more of the following values: an indication ofwhether or not the user is experiencing the abnormal medical event, avalue indicative of an extent to which the user is experiencing theabnormal medical event, a duration since the onset of the abnormalmedical event, and a duration until an onset of the abnormal medicalevent.

When the blood flow on both sides of the head and/or body are monitored,asymmetric changes may be recognized. These changes are typicallydifferent from symmetric changes that can be caused by factors such asphysical activity (which typically affects the blood flow on both sidesin the same way). An asymmetric change to the blood flow can mean thatone side has been affected by an event, such as a stroke, which does notinfluence the other side. In one example, the asymmetric change to bloodflow involves a change in blood flow velocity on left side of the headthat is at least 10% greater or 10% lower than a change in blood flowvelocity on one right side of the head. In another example, theasymmetric change to blood flow involves a change in the volume of bloodthe flows during a certain period in the left side of the head that isat least 10% greater or 10% lower than the volume of blood that flowsduring the certain period in the right side of the head. In yet anotherexample, the asymmetric change to blood flow involves a change in thedirection of the blood flow on one side of the head (e.g., as a resultof a stroke), which is not necessarily observed at the symmetriclocation on the other side of the head.

Referring to an asymmetrical change to blood flow as being “recognizablein PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2)” means that valuesextracted from PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2) provide anindication that an asymmetric change to the blood flow has occurred.That is, a difference that has emerged in the PPG_(SR1), PPG_(SR2),PPG_(SL1), and PPG_(SL2) may reflect a change in blood flow velocity onone side of the head, a change in blood flow volume, and/or a change inblood flow direction, as described in the examples above. It is to benoted, that the change in blood flow does not need to be directlyquantified from the values PPG_(SR1), PPG_(SR2), PPG_(SL1), andPPG_(SL2) in order for it to be “recognizable in PPG_(SR1), PPG_(SR2),PPG_(SL1), and PPG_(SL2)”. Rather, in some embodiments, feature valuesgenerated based on PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2) may beused by a machine learning-based predictor to detect a phenomenon, suchas the abnormal medical event, which is associated with the asymmetricalchange in blood flow.

Having multiple PPG signals, measured at different sides of the head,can assist in detecting an asymmetric change to blood flow in differentways. In one example, an asymmetric change in blood flow may becharacterized in an increase in volume at a certain region and/or sideof the head, compared to other regions and/or the other side of thehead. In this example, the amplitude of the PPG signal at the certainregion may show a greater increase compared to increases observed withPPG signals at other regions and/or on the other side of the head. Inanother example, an asymmetric change in blood flow may be characterizedby a decrease in blood velocity at a certain region and/or side of thehead, compared to other regions and/or the other side of the head. Inthis example, a pulse arrival time (PAT) at the certain region mayexhibit a larger delay compared to the delay of PATs at other regionsand/or at the other side of the head. In still another example, anasymmetric change in blood flow may be characterized by a change in adirection of blood flow at a certain region, compared to other regionsand/or the symmetric region on the other side of the head. In thisexample, an order at which pulse waves arrive at different regions (asevident by PPG signals at the different regions) may be indicative ofthe direction of blood flow. Thus, a change in the arrival order ofpulse waves on one side of the head, which does not occur at regions onthe other side of the head, may indicate an asymmetrical change of adirection of blood flow.

In some embodiments, the computer detects the abnormal medical event byutilizing previously taken PPG signals of the user (i.e., previouslytaken PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2)), from a periodthat precedes the current abnormal medical event being detected at thattime. This enables an asymmetrical change to be observed, since itprovides a baseline according to which it is possible to compare currentPPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2), such that it may bedetermined that a change to blood flow on one side of the head is notthe same as a change on the other side of the head. In some embodiments,previously taken PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2) areutilized to calculate a baseline blood flow, such as values representingthe extent of blood flow at the different sides of the head, and/or atdifferent regions (e.g., ROI_(R1), ROI_(R2), ROI_(L1) and ROI_(L2)).Optionally, calculating the baseline blood flow may be done based onpreviously taken PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2) thatwere measured at least an hour before the abnormal medical event isdetected. Optionally, the previously taken PPG_(SR1), PPG_(SR2),PPG_(SL1), and PPG_(SL2) include PPG signals measured at least a daybefore the abnormal medical event is detected. Optionally, thepreviously taken PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2) includePPG signals measured more than a week before the abnormal medical eventis detected.

A baseline for the blood flow may be calculated in various ways. In afirst example, the baseline is a function of the average measurements ofthe user (which include previously taken PPG_(SR1), PPG_(SR2),PPG_(SL1), and PPG_(SL2)), which were taken before the occurrence of theabnormal medical event. In a second example, the baseline may be afunction of the situation the user is in, such that previousmeasurements taken during similar situations are weighted higher thanprevious measurements taken during less similar situations. A PPG signalmay show different characteristics in different situations because ofthe different mental and/or physiological states of the user in thedifferent situations. As a result, a situation-dependent baseline canimprove the accuracy of detecting the abnormal medical event. In a thirdexample, the baseline may be a function of an intake of some substance,such that previous measurements taken after consuming similar substancesare weighted higher than previous measurements taken after not consumingthe similar substances and/or after consuming less similar substances. APPG signal may show different characteristics after the user consumesdifferent substances because of the different mental and/orphysiological states the user may be in after consuming the substances,especially when the substances include things such as medications,drugs, alcohol, and/or certain types of food. As a result, asubstance-dependent baseline can improve the accuracy of detecting theabnormal medical event.

There are various types of abnormal medical events that may be detectedbased on PPG signals that reflect an asymmetrical change to blood flow,which is recognizable in PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2)of the user.

In some embodiments, the abnormal medical event may involve the userhaving a cerebrovascular accident, which is also known as having astroke. An occurrence of a stroke often has the following effect on aperson. A blood clot (in the case of ischemic stroke) or the rapturedartery (in the case of a hemorrhagic stroke) changes the blood flow tocertain regions of the brain. One or more of several mechanisms may bethe cause of changes to blood flow that are observed following an onsetof a stroke. Blood flow may change due to a stroke because of flaccidmuscles (on one side of the face) that use less oxygen and demand lessblood. In such an event, local regulation mechanisms may generatesignals to the smooth muscles that decrease the diameter of the arteries(which can reduce blood flow). Additionally or alternatively, blood flowmay change due to a stroke because of nerve control changes that occurdue to reduced blood flow to the brain (a neurogenic mechanism); thesame nerves that control the muscles can also be involved in the controlof the constriction/dilation of blood vessels. Another possible cause ofchanges to blood flow involves obstruction-related passive changes.Blood that flows through the major vessels (in the base of the brain itis either the carotid (front) or vertebral (back) arteries, must flowout through one of the branches. When one pathway is blocked orrestricted (due to the stroke), more blood has to go through collateralpathways (which may change the blood flow). Thus, changes to the bloodflow in the face (and other areas of the head), especially if they areasymmetric, can be early indicators of a stroke. An event of a strokemay be detected in various ways, some which are described in thefollowing examples.

In one embodiment, the abnormal medical event is an ischemic stroke, andthe deviation (of PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2)compared to a baseline based on the previous measurements of PPG_(SR1),PPG_(SR2), PPG_(SL1), and PPG_(SL2) of the user) involves an increase inasymmetry between blood flow on the left side of the head and blood flowon the right side of the head, with respect to a baseline asymmetrylevel between blood flow on the left side of the head and blood flow onthe right side of the head (as determined based on the previousmeasurements). In some embodiments, the term “ischemic stroke” may alsoinclude Transient Ischemic Attack (TIA), known as “mini stroke”.

In another embodiment, the abnormal medical event is an ischemic stroke,and the deviation (of PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2)compared to a baseline based on the previous measurements of PPG_(SR1),PPG_(SR2), PPG_(SL1), and PPG_(SL2) of the user) involves a monotonicincrease in asymmetry between blood flow at ROI_(R1) and ROI_(R2), withrespect to a baseline asymmetry of the user (between blood flow atROI_(R1) and ROI_(R2)) during a period longer than 10 minutes.Optionally, ROI_(R1) is located in proximity of the mastoid processbehind the right ear, and ROI_(R2) is located before of the right ear.

In yet another embodiment, the abnormal medical event is an ischemicstroke, and the deviation involves an increase in variation betweenblood flow at ROI_(R1) and ROI_(R2), with respect to a baselinevariation of the user between blood flow at ROI_(R1) and ROI_(R2).Optionally, the computer suggests the user to take images of at leastone of the retinas, responsive to detecting the increase in variation.The computer may then compare the images of the retinas with previouslytaken images of the user's retinas, and utilize such a comparison toimprove the accuracy of detecting whether the user has suffered theischemic stroke. Optionally, the comparison of the images of the retinasmay take into account parameters such as the diameter of retinalarteries, swelling of the boundaries of the optic disk, and/or blurringof the boundaries of the optic disk. The images of the retinas may betaken by any known and/or to be invented appropriate device.

In some embodiments, the abnormal medical event may involve the userhaving a migraine or another form of headache. With migraines and otherheadaches, vasoconstriction of facial or cranial blood vessels may leadto asymmetric changes in blood flow between the left and right sides ofthe head. Compensatory mechanisms may change smooth muscle constrictionaround blood vessels, further exacerbating this asymmetry. Thisvasoconstriction can lead to differential surface blood flow, musclecontraction, and facial temperature changes, leading to asymmetric bloodflow. As each individual's particular patterns of vasoconstriction wouldbe unique to the individual, the asymmetric phenomena may be differentfor different users. Thus, measuring deviation from the user's baselineblood flow patterns may increase the accuracy of detecting theseasymmetric phenomena, in some embodiments. Additionally, the time courseof migraine or headache usually involves an occurrence over the courseof minutes to hours (from the onset of changes to blood flow), andusually occurs with a characteristic pattern, allowing it to bedifferentiated from signs of other medical, artificial or externalcauses, which manifest different patterns of blood flow and/or timecourses.

In one embodiment, the abnormal medical event is a migraine attack, andthe deviation (of PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2)compared to a baseline based on the previous measurements of PPG_(SR1),PPG_(SR2), PPG_(SL1), and PPG_(SL2) of the user) is indicative of apattern of a certain change to facial blood flow, which is associatedwith a pattern of a change to facial blood flow of at least one previousmigraine attack, determined based on data comprising previous PPG_(SR1),PPG_(SR2), PPG_(SL1), and PPG_(SL2), which were measured starting fromat least 5 minutes before the previous migraine attack. In thisembodiment, the time of the beginning of the previous migraine attackcorresponds to the time at which the user became aware of the migraineattack.

In another embodiment, the abnormal medical event is headache, and thedeviation is indicative of at least one of: a change in directionalityof facial blood flow, and an asymmetrical reduction in blood flow to oneside of the face (for a period lasting more than one minute). For somepeople, a migraine attack and/or a headache may cause a change indirectionality of facial blood flow because the pulse propagation acrossthe face arrives at one side before it arrives to the other side. In oneexample, the changes in directionality of facial blood flow arecalculated from phase variations between PPG_(SR1), PPG_(SR2),PPG_(SL1), and PPG_(SL2) relative to a baseline for the user. For somepeople, vasoconstriction caused by a migraine attack and/or a headachemay affect the amplitude of the PPG signals, such as decreasing ofamplitudes of the PPG signals in a certain region. In one example, thereduction in blood flow to one side of the face is calculated fromchanges between amplitudes of PPG_(SR1), PPG_(SR2), PPG_(SL1), andPPG_(SL2) relative to the baseline.

In some embodiments, the abnormal medical event may involve the usersuffering from an infection. Inflammatory conditions, such ascellulitis, dermatitis and ear infection, originate in infection orinflammation in one particular region of the face, causing vasodilationleading to a facial asymmetry originating from phenomena such asincrease in swelling, redness and warmth, which are detectable only inthe vicinity of the infection. As each individual's baseline facialblood flow and coloration is different, comparing current measurementswith the baseline may allow accurate identification of vasodilationresulting from an inflammatory condition. The time course of suchinflammatory conditions would usually occur over the course of hours todays, allowing it to be differentiated from other medical or artificialphenomena, which may have similar signs but over a different timecourse.

Since inflammation often causes temperatures at the infected region torise, accuracy of detection of some abnormal medical events may increaseby measuring the temperature at different regions on the head. In oneembodiment, the system configured to detect an abnormal medical eventincludes right and left head-mounted thermometers, located at least 2 cmto the right and to the left of a vertical symmetry axis that dividesthe face, respectively. The head-mounted thermometers may be contactthermometers, such as thermistors, and/or non-contact thermal cameras.Optionally, the head-mounted thermometers measure ROIs on one or more ofthe following regions: the forehead, the temples, behind the ear, thecheeks, the nose, and the mouth. The right and left head-mountedthermometers take right and left temperature measurements, respectively.Optionally, the computer detects the abnormal medical event also basedon a deviation of the right and left temperature measurements from abaseline temperature for the user, where the baseline temperature forthe user is calculated based on data comprising previous right and lefttemperature measurements of the user, taken more than a day before theabnormal medical event. Optionally, the abnormal medical event detectedin this embodiment is selected from a set comprising cellulitis anddermatitis.

In one embodiments, the system configured to detect an abnormal medicalevent may optionally include right and left head-mounted thermometers,located less than 4 cm from the right and left earlobes, respectively,which provide right and left temperature measurements, respectively.Optionally, the computer detects the abnormal medical event also basedon a deviation of the right and left temperature measurements from abaseline temperature for the user, where the baseline temperature forthe user is calculated based on data comprising previous right and lefttemperature measurements of the user, taken more than a day before theabnormal medical event. In one example, the abnormal medical event isear infection. In another example, the abnormal medical event iscerebrovascular accident. In still another example, the abnormal medicalevent is mastoiditis.

Obtaining the PPG signals PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2)from measurements taken by the at least one right-side head-mounteddevice and the at least one left-side head-mounted device may involve,in some embodiments, performing various preprocessing operations inorder to assist in calculations and/or in extraction of the PPG signals.Optionally, the measurements may undergo various preprocessing stepsprior to being used by the computer to detect the abnormal medicalevent, and/or as part of the process of the detection of the abnormalmedical event. Some non-limiting examples of the preprocessing include:normalization of pixel intensities (e.g., to obtain a zero-mean unitvariance time series signal), and conditioning a time series signal byconstructing a square wave, a sine wave, or a user defined shape, suchas that obtained from an ECG signal or a PPG signal as described in U.S.Pat. No. 8,617,081.

In some embodiments, in which the at least one right-side head-mounteddevice and/or at least one left-side head-mounted device are cameras,images taken by the cameras may undergo various preprocessing to improvethe signal, such as color space transformation (e.g., transforming RGBimages into a monochromatic color or images in a different color space),blind source separation using algorithms such as independent componentanalysis (ICA) or principal component analysis (PCA), and variousfiltering techniques, such as detrending, bandpass filtering, and/orcontinuous wavelet transform (CWT). Various preprocessing techniquesknown in the art that may assist in extracting an PPG signals fromimages are discussed in Zaunseder et al. (2018), “Cardiovascularassessment by imaging photoplethysmography-a review”, BiomedicalEngineering 63(5), 617-634. An example of preprocessing that may be usedin some embodiments is given in U.S. Pat. No. 9,020,185, titled “Systemsand methods for non-contact heart rate sensing”, which describes how atimes-series signals obtained from video of a user can be filtered andprocessed to separate an underlying pulsing signal by, for example,using an ICA algorithm.

In some embodiments, detection of the abnormal medical event may involvecalculation of pulse arrival times (PATs) at one or more of the regionsROI_(R1), ROI_(R2), ROI_(L1) and ROI_(L2). Optionally, a PAT calculatedfrom an PPG signal represents a time at which the value representingblood volume (in the waveform represented in the PPG) begins to rise(signaling the arrival of the pulse). Alternatively, the PAT may becalculated as a different time, with respect to the pulse waveform, suchas the time at which a value representing blood volume reaches a maximumor a certain threshold, or the PAT may be the average of the time theblood volume is above a certain threshold. Another approach that may beutilized to calculate a PAT from an iPPG signal is described in Sola etal. “Parametric estimation of pulse arrival time: a robust approach topulse wave velocity”, Physiological measurement 30.7 (2009): 603, whichdescribe a family of PAT estimators based on the parametric modeling ofthe anacrotic phase of a pressure pulse.

Detection of the abnormal medical event may involve the computerutilizing an approach that may be characterized as involving machinelearning. In some embodiments, such a detection approach may involve thecomputer generating feature values based on data that includes PPGsignals (i.e., PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2) of theuser) and optionally other data, and then utilizing a previously trainedmodel to calculate one or more values indicative of whether the user isexperiencing the abnormal medical event (which may be any one of theexamples of values mentioned further above as being calculated by thecomputer for this purpose). It is to be noted that when the computercalculates a value that is indicative of the user having the abnormalmedical event, at least some of the feature values may reflect the factthat an asymmetrical change to blood flow had occurred. Optionally, theadditional data used to generate at least some of the feature valuesincludes previous measurements of PPG_(SR1), PPG_(SR2), PPG_(SL1), andPPG_(SL2) of the user, and/or measurements from additional sensorsand/or data sources as discussed below.

Feature values generated based on PPG signals may include various typesof values, which may be indicative of dynamics of the blood flow at therespective regions to which the PPG signals correspond. Optionally,these feature values may relate to properties of a pulse waveform, whichmay be a specific pulse waveform (which corresponds to a certain beat ofthe heart), or a window of pulse waveforms (e.g., an average property ofpulse waveforms in a certain window of time). Some examples of featurevalues that may be generated based on a pulse waveform include: the areaunder the pulse waveform, the amplitude of the pulse waveform, aderivative and/or second derivative of the pulse waveform, a pulsewaveform shape, pulse waveform energy, and pulse transit time (to therespective ROI). Some additional examples of features may be indicativeone or more of the following: a magnitude of a systolic peak, amagnitude of a diastolic peak, duration of the systolic phase, andduration of the diastolic phase. Additional examples of feature valuesmay include properties of the cardiac activity, such as the heart rateand heart rate variability (as determined from the PPG signal).Additionally, some feature values may include values of otherphysiological signals that may be calculated based on PPG signals, suchas blood pressure and cardiac output.

It is to be noted that the aforementioned feature values may becalculated in various ways. In one example, some feature values arecalculated for each PPG signal individually. In another example, somefeature values are calculated after normalizing a PPG signal withrespect to previous measurements from the corresponding PPG device usedto measure the PPG signal. In other examples, at least some of thefeature values may be calculated based on an aggregation of multiple PPGsignals (e.g., different pixels/regions in images captured by an iPPGdevice), or by aggregating values from multiple contact PPG devices.

In some embodiments, at least some of the feature values may representcomparative values, which provide an indication of the difference inblood flow, and/or in some other property that may be derived from a PPGsignal, between various regions on the head. Optionally, such featurevalues may assist in detecting asymmetrical blood flow (and/or changesthereto). In one example, the feature values include a certain featurevalue indicative of a difference in maximal amplitudes of one or more ofthe following pairs of PPG signals: (i) PPG_(SR1) and PPG_(SR2), (ii)PPG_(SR1) and PPG_(SL1), and (iii) PPG_(SR1) and PPG_(SL2). In anotherexample, the feature values include a certain feature value indicativeof a difference in a pulse arrival time between the following pairs ofregions of interest: (i) ROI_(R1) and ROI_(R2), (ii) ROI_(R1) andROI_(L1), and (iii) ROI_(R1) and ROI_(L2).

In some embodiments, at least some of the feature values describeproperties of pulse waveforms (e.g., various types of feature valuesmentioned above), which are derived from the previous measurements ofPPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2) of the user. Optionally,these feature values may include various blood flow baselines for theuser, which correspond to a certain situation the user was in when theprevious measurements were taken.

In some embodiments, at least some of the feature values may be “raw” orminimally processed measurements of the at least one right-sidehead-mounted device and/or at least one left-side head-mounted device.In one example, at least some of the feature values may be valuesobtained from contact PPG devices. In another example, at least some ofthe feature values may be pixel values obtained by inward-facinghead-mounted cameras. Optionally, the pixel values may be provided asinput to functions in order to generate at feature values that arelow-level image-based features. Some examples of low-level features,which may be derived from images, include feature generated using Gaborfilters, local binary patterns (LBP) and their derivatives, algorithmssuch as SIFT and/or SURF (and their derivatives), image keypoints,histograms of oriented gradients (HOG) descriptors, and products ofstatistical procedures such independent component analysis (ICA),principal component analysis (PCA), or linear discriminant analysis(LDA). Optionally, one or more of the feature values may be derived frommultiple images taken at different times, such as volume local binarypatterns (VLBP), cuboids, and/or optical strain-based features. In oneexample, one or more of the feature values may represent a differencebetween values of pixels at one time t and values of other pixels at adifferent region at some other time t+x (which, for example, can helpdetect different arrival times of a pulse wave).

In some embodiments, at least some feature values may be generated basedon other data sources (in addition to PPG signals). In some examples, atleast some feature values may be generated based on other sensors, suchas movement sensors (which may be head-mounted, wrist-worn, or carriedby the user some other way), head-mounted thermal cameras (e.g., asmentioned above), or other sensors used to measure the user. In otherexamples, at least some feature values may be indicative ofenvironmental conditions, such as the temperature, humidity, and/orextent of illumination (e.g., as obtained utilizing an outward-facinghead-mounted camera). Additionally, some feature values may beindicative of physical characteristics of the user, such as age, sex,weight, Body Mass Index (BMI), skin tone, and other characteristicsand/or situations the user may be in (e.g., level of tiredness,consumptions of various substances, etc.)

Stress is a factor that can influence the diameter of the arteries, andthus influence calculated values that relate to the PPG signals and/orblood flow. In one embodiment, the computer receives a value indicativeof a stress level of the user, and generates at least one of the featurevalues based on the received value. Optionally, the value indicative ofthe stress level is obtained using a thermal camera. In one example, thesystem may include an inward-facing head-mounted thermal camera thattakes measurements of a periorbital region of the user, where themeasurements of a periorbital region of the user are indicative of thestress level of the user. In another example, the system includes aninward-facing head-mounted thermal camera that takes measurements of aregion on the forehead of the user, where the measurements of the regionon the forehead of the user are indicative of the stress level of theuser. In still another example, the system includes an inward-facinghead-mounted thermal camera that takes measurements of a region on thenose of the user, where the measurements of the region on the nose ofthe user are indicative of the stress level of the user.

Hydration is a factor that affects blood viscosity, which can affect thespeed at which the blood flows in the body. In one embodiment, thecomputer receives a value indicative of a hydration level of the user,and generates at least one of the feature values based on the receivedvalue. Optionally, the system includes an additional camera that detectsintensity of radiation that is reflected from a region of exposed skinof the user, where the radiation is in spectral wavelengths chosen to bepreferentially absorbed by tissue water. In one example, saidwavelengths are chosen from three primary bands of wavelengths ofapproximately 1100-1350 nm, approximately 1500-1800 nm, andapproximately 2000-2300 nm. Optionally, measurements of the additionalcamera are utilized by the computer as values indicative of thehydration level of the user.

The following are examples of embodiments that utilize additional inputsto generate feature values used to detect the abnormal medical event. Inone embodiment, the computer receives a value indicative of atemperature of the user's body, and generates at least one of thefeature values based on the received value. In another embodiment, thecomputer receives a value indicative of a movement of the user's body,and generates at least one of the feature values based on the receivedvalue. For example, the computer may receive the input form ahead-mounted Inertial Measurement Unit (IMU) that includes a combinationof accelerometers, gyroscopes, and optionally magnetometers, and/or anIMU in a mobile device carried by the user. In yet another embodiment,the computer receives a value indicative of an orientation of the user'shead, and generates at least one of the feature values based on thereceived value. For example, the computer may receive the valuesindicative of the head's orientation from an outward-facing head-mountedcamera, and/or from a nearby non-wearable video camera. In still anotherembodiment, the computer receives a value indicative of consumption of asubstance by the user, and generates at least one of the feature valuesbased on the received value. Optionally, the substance comprises avasodilator and/or a vasoconstrictor.

The model utilized to detect the abnormal medical event may begenerated, in some embodiments, based on data obtained from one or moreusers, corresponding to times in which the one or more users were notaffected by the abnormal medical event, and additional data obtainedwhile the abnormal medical event occurred and/or following that time.Thus, this training data may reflect PPG signals and/or blood flow bothat normal times, and changes to PPG signals and/or blood flow that mayensue due to the abnormal medical event. This data may be used togenerate samples, each sample including feature values generated basedon PPG signals of a user and optionally additional data (as describedabove), and a label. The PPG signals include PPG_(SR1), PPG_(SR2),PPG_(SL1), and PPG_(SL2) of the user at a certain time, and optionallyprevious PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2) of the user,taken before the certain time. The label is a value related to thestatus of the abnormal medical event. For example, the label may beindicative of whether the user, at the certain time, experienced theabnormal medical event. In another example, the label may be indicativeof the extent or severity of the abnormal medical event at the certaintime. In yet another example, the label may be indicative of theduration until an onset of the abnormal medical event. In still anotherexample, the label may be indicative of the duration that has elapsedsince the onset of the abnormal medical event.

In some embodiments, the model used by the computer to detect theabnormal medical event from measurements of a certain user may begenerated, at least in part, based on data that includes previousmeasurements of the certain user (and as such, may be consideredpersonalized to some extent for the certain user). Additionally oralternatively, in some embodiments, the model may be generated based ondata of other users. Optionally, the data used to train the model mayinclude data obtained from a diverse set of users (e.g., users ofdifferent ages, weights, sexes, preexisting medical conditions, etc.).Optionally, the data used to train the model which is used to detect theabnormal medical event with a certain user includes data of other userswith similar characteristics to the certain user (e.g., similar weight,age, sex, height, and/or preexisting condition).

In order to achieve a robust model, which may be useful for detectingthe abnormal medical event for a diverse set of conditions, in someembodiments, the samples used for the training of the model may includesamples based on data collected when users were in different conditions.Optionally, the samples are generated based on data collected ondifferent days, while indoors and outdoors, and while differentenvironmental conditions persisted. In one example, the model is trainedon samples generated from a first set of training data taken duringdaytime, and is also trained on other samples generated from a secondset of training data taken during nighttime. In a second example, themodel is trained on samples generated from a first set of training datataken while users were exercising and moving, and is also trained onother samples generated from a second set of data taken while users weresitting and not exercising.

Utilizing the model to detect the abnormal medical event may involve thecomputer performing various operations, depending on the type of model.The following are some examples of various possibilities for the modeland the type of calculations that may be accordingly performed by thecomputer, in some embodiments, in order to calculate a value indicativeof whether the user was experiencing the abnormal medical event: (a) themodel comprises parameters of a decision tree. Optionally, the computersimulates a traversal along a path in the decision tree, determiningwhich branches to take based on the feature values. A value indicativeof whether the user was experiencing the abnormal medical event may beobtained at the leaf node and/or based on calculations involving valueson nodes and/or edges along the path; (b) the model comprises parametersof a regression model (e.g., regression coefficients in a linearregression model or a logistic regression model). Optionally, thecomputer multiplies the feature values (which may be considered aregressor) with the parameters of the regression model in order toobtain the value indicative of whether the user was experiencing theabnormal medical event; and/or (c) the model comprises parameters of aneural network. For example, the parameters may include values definingat least the following: (i) an interconnection pattern between differentlayers of neurons, (ii) weights of the interconnections, and (iii)activation functions that convert each neuron's weighted input to itsoutput activation. Optionally, the computer provides the feature valuesas inputs to the neural network, computes the values of the variousactivation functions and propagates values between layers, and obtainsan output from the network, which is the value indicative of whether theuser was experiencing the abnormal medical event.

In some embodiments, a machine learning approach that may be applied tocalculating a value indicative of whether the user is experiencing anabnormal medical event may be characterized as “deep learning”. In oneembodiment, the model may include parameters describing multiple hiddenlayers of a neural network. Optionally, the model may include aconvolution neural network (CNN). In one example, the CNN may beutilized to identify certain patterns in video images, such as thepatterns of corresponding to blood volume effects andballistocardiographic effects of the cardiac pulse. Due to the fact thatcalculating a value indicative of whether the user is experiencing theabnormal medical event may be based on multiple, possibly successive,images that display a certain pattern of change over time (i.e., acrossmultiple frames), these calculations may involve retaining stateinformation that is based on previous images. Optionally, the model mayinclude parameters that describe an architecture that supports such acapability. In one example, the model may include parameters of arecurrent neural network (RNN), which is a connectionist model thatcaptures the dynamics of sequences of samples via cycles in thenetwork's nodes. This enables RNNs to retain a state that can representinformation from an arbitrarily long context window. In one example, theRNN may be implemented using a long short-term memory (LSTM)architecture. In another example, the RNN may be implemented using abidirectional recurrent neural network architecture (BRNN).

The following method for detecting an abnormal medical event may be usedby systems modeled according to FIG. 1a or FIG. 1b . The steps describedbelow may be performed by running a computer program having instructionsfor implementing the method. Optionally, the instructions may be storedon a computer-readable medium, which may optionally be a non-transitorycomputer-readable medium. In response to execution by a system includinga processor and memory, the instructions cause the system to perform thefollowing steps:

In Step 1, measuring, utilizing at least one right-side head-mounteddevice, at least two signals indicative of photoplethysmographic signals(PPG_(SR1) and PPG_(SR2), respectively) at first and second regions ofinterest (ROI_(R1) and ROI_(R2), respectively) on the right side of auser's head. ROI_(R1) and ROI_(R2) are located at least 2 cm apart.

In Step 2, measuring, utilizing at least one left-side head-mounteddevice, at least two signals indicative of photoplethysmographic signals(PPG_(SL1) and PPG_(SL2), respectively) at first and second regions ofinterest (ROI_(L1) and ROI_(L2), respectively) on the left side of theuser's head. ROI_(L1) and ROI_(L2) are located at least 2 cm apart.

And in Step 3, detecting the abnormal medical event based on anasymmetrical change to blood flow recognizable in PPG_(SR1), PPG_(SR2),PPG_(SL1), and PPG_(SL2), relative to a baseline based on previousmeasurements of PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2) of theuser, taken before the abnormal medical event.

In some embodiments, detecting the abnormal medical event is doneutilizing a machine learning-based approach. Optionally, the methodincludes the following steps: generating feature values based on datacomprising: (i) PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2) of theuser, and (ii) the previous measurements of PPG_(SR1), PPG_(SR2),PPG_(SL1), and PPG_(SL2) of the user; and utilizing a model tocalculate, based on the feature values, a value indicative of whetherthe user is experiencing the abnormal medical event.

The following is description of additional aspects of embodiments ofsystems configured to detect an abnormal medical event, as well asadditional embodiments for various systems that may detect physiologicalresponses based on thermal measurements and/or other sources of data.

“Visible-light camera” refers to a non-contact device designed to detectat least some of the visible spectrum, such as a video camera withoptical lenses and CMOS or CCD sensor. A “thermal camera” refers hereinto a non-contact device that measures electromagnetic radiation havingwavelengths longer than 2500 nanometer (nm) and does not touch itsregion of interest (ROI). A thermal camera may include one sensingelement (pixel), or multiple sensing elements that are also referred toherein as “sensing pixels”, “pixels”, and/or focal-plane array (FPA). Athermal camera may be based on an uncooled thermal sensor, such as athermopile sensor, a microbolometer sensor (where microbolometer refersto any type of a bolometer sensor and its equivalents), a pyroelectricsensor, or a ferroelectric sensor.

A reference to a “camera” herein may relate to various types of devices.In one example, a camera may be a visible-light camera. In anotherexample, a camera may capture light in the ultra-violet range. Inanother example, a camera may capture near infrared radiation (e.g.,wavelengths between 750 and 2000 nm). And in still another example, acamera may be a thermal camera.

In some embodiments, a device, such as a camera, may be positioned suchthat it occludes an ROI on the user's face, while in other embodiments,the device may be positioned such that it does not occlude the ROI.Sentences in the form of “the system/camera does not occlude the ROI”indicate that the ROI can be observed by a third person located in frontof the user and looking at the ROI, such as illustrated by all the ROIsin FIG. 23 and FIG. 27. Sentences in the form of “the system/cameraoccludes the ROI” indicate that some of the ROIs cannot be observeddirectly by that third person, such as ROIs 19 and 37 that are occludedby the lenses in FIG. 17a , and ROIs 97 and 102 that are occluded bycameras 91 and 96, respectively, in FIG. 25. Additionally, when thecontext is clear, an ROI (region of interest) may be referred to as a“region” (which is on the body or face of the user).

Although many of the disclosed embodiments can use occluding cameras(with or without a light source) successfully, in certain scenarios,such as when using an HMS on a daily basis and/or in a normal day-to-daysetting, using cameras that do not occlude their ROIs on the face mayprovide one or more advantages to the user, to the HMS, and/or to thecameras, which may relate to one or more of the following: esthetics,better ventilation of the face, reduced weight, simplicity to wear, andreduced likelihood to being tarnished.

The term “inward-facing head-mounted camera” refers to a cameraconfigured to be worn on a user's head and to remain pointed at its ROI,which is on the user's face, also when the user's head makes angular andlateral movements (such as movements with an angular velocity above 0.1rad/sec, above 0.5 rad/sec, and/or above 1 rad/sec). A head-mountedcamera (which may be inward-facing and/or outward-facing) may bephysically coupled to a frame worn on the user's head, may be attachedto eyeglass using a clip-on mechanism (configured to be attached to anddetached from the eyeglasses), may be physically coupled to a hat or ahelmet, or may be mounted to the user's head using any other knowndevice that keeps the camera in a fixed position relative to the user'shead also when the head moves. Sentences in the form of “cameraphysically coupled to the frame” mean that the camera moves with theframe, such as when the camera is fixed to (or integrated into) theframe, or when the camera is fixed to (or integrated into) an elementthat is physically coupled to the frame.

Sentences in the form of “a frame configured to be worn on a user'shead” or “a frame worn on a user's head” refer to a mechanical structurethat loads more than 50% of its weight on the user's head. For example,an eyeglasses frame may include two temples connected to two rimsconnected by a bridge; the frame in Oculus Rift™ includes the foamplaced on the user's face and the straps; and the frames in GoogleGlass™ and Spectacles by Snap Inc. are similar to eyeglasses frames.Additionally or alternatively, the frame may connect to, be affixedwithin, and/or be integrated with, a helmet (e.g., sports, motorcycle,bicycle, and/or combat helmets) and/or a brainwave-measuring headset.

When a camera is inward-facing and head-mounted, challenges faced bysystems known in the art that are used to acquire images, which includenon-head-mounted cameras, may be simplified and even eliminated withsome of the embodiments described herein. Some of these challenges mayinvolve dealing with complications caused by movements of the user,image registration, ROI alignment, tracking based on hot spots ormarkers, and motion compensation.

In various embodiments, cameras are located close to a user's face, suchas at most 2 cm, 5 cm, 10 cm, 15 cm, or 20 cm from the face. Thedistance from the face/head in sentences such as “a camera located lessthan 10 cm from the face/head” refers to the shortest possible distancebetween the camera and the face/head. The head-mounted cameras used invarious embodiments may be lightweight, such that each camera weighsbelow 10 g, 5 g, 1 g, and/or 0.5 g.

The following figures show various examples of HMSs equipped withhead-mounted cameras. FIG. 17a illustrates various inward-facinghead-mounted cameras coupled to an eyeglasses frame 15. Cameras 10 and12 capture regions 11 and 13 on the forehead, respectively. Cameras 18and 36 capture regions on the periorbital areas 19 and 37, respectively.The HMS further includes an optional computer 16, which may include aprocessor, memory, a battery and/or a communication module. FIG. 17billustrates a similar HMS in which inward-facing head-mounted cameras 48and 49 capture regions 41 and 41, respectively. Cameras 22 and 24capture regions 23 and 25, respectively. Camera 28 captures region 29.And cameras 26 and 43 capture regions 38 and 39, respectively.

FIG. 18 illustrates inward-facing head-mounted cameras coupled to anaugmented reality device such as Microsoft HoloLens™. FIG. 19illustrates head-mounted cameras coupled to a virtual reality devicesuch as Facebook's Oculus Rift™. FIG. 20 is a side view illustration ofhead-mounted cameras coupled to an augmented reality device such asGoogle Glass™. FIG. 21 is another side view illustration of head-mountedcameras coupled to a sunglasses frame.

FIG. 22 to FIG. 25 illustrate HMSs configured to capture various ROIsrelevant to some of the embodiments describes herein. FIG. 22illustrates a frame 35 that mounts inward-facing head-mounted cameras 30and 31 that capture regions 32 and 33 on the forehead, respectively.FIG. 23 illustrates a frame 75 that mounts inward-facing head-mountedcameras 70 and 71 that capture regions 72 and 73 on the forehead,respectively, and inward-facing head-mounted cameras 76 and 77 thatcapture regions 78 and 79 on the upper lip, respectively. FIG. 24illustrates a frame 84 that mounts inward-facing head-mounted cameras 80and 81 that capture regions 82 and 83 on the sides of the nose,respectively. And FIG. 25 illustrates a frame 90 that includes (i)inward-facing head-mounted cameras 91 and 92 that are mounted toprotruding arms, and capture regions 97 and 98 on the forehead,respectively, (ii) inward-facing head-mounted cameras 95 and 96, whichare also mounted to protruding arms, which capture regions 101 and 102on the lower part of the face, respectively, and (iii) head-mountedcameras 93 and 94 that capture regions on the periorbital areas 99 and100, respectively.

FIG. 26 to FIG. 29 illustrate various inward-facing head-mounted camerashaving multi-pixel sensors (FPA sensors), configured to capture variousROIs relevant to some of the embodiments describes herein. FIG. 26illustrates head-mounted cameras 120 and 122 that capture regions 121and 123 on the forehead, respectively, and head-mounted camera 124 thatcaptures region 125 on the nose. FIG. 27 illustrates head-mountedcameras 126 and 128 that capture regions 127 and 129 on the upper lip,respectively, in addition to the head-mounted cameras already describedin FIG. 26. FIG. 28 illustrates head-mounted cameras 130 and 132 thatcapture larger regions 131 and 133 on the upper lip and the sides of thenose, respectively. And FIG. 29 illustrates head-mounted cameras 134 and137 that capture regions 135 and 138 on the right and left cheeks andright and left sides of the mouth, respectively, in addition to thehead-mounted cameras already described in FIG. 28.

In some embodiments, the head-mounted cameras may be physically coupledto the frame using a clip-on device configured to be attached/detachedfrom a pair of eyeglasses in order to secure/release the device to/fromthe eyeglasses, multiple times. The clip-on device holds at least aninward-facing camera, a processor, a battery, and a wirelesscommunication module. Most of the clip-on device may be located in frontof the frame (as illustrated in FIG. 30b , FIG. 31b , and FIG. 34), oralternatively, most of the clip-on device may be located behind theframe, as illustrated in FIG. 33b and FIG. 32 b.

FIG. 30a , FIG. 30b , and FIG. 30c illustrate two right and left clip-ondevices 141 and 142, respectively, configured to attached/detached froman eyeglasses frame 140. The clip-on device 142 includes aninward-facing head-mounted camera 143 pointed at a region on the lowerpart of the face (such as the upper lip, mouth, nose, and/or cheek), aninward-facing head-mounted camera 144 pointed at the forehead, and otherelectronics 145 (such as a processor, a battery, and/or a wirelesscommunication module). The clip-on devices 141 and 142 may includeadditional cameras illustrated in the drawings as black circles.

FIG. 31a and FIG. 31b illustrate a clip-on device 147 that includes aninward-facing head-mounted camera 148 pointed at a region on the lowerpart of the face (such as the nose), and an inward-facing head-mountedcamera 149 pointed at the forehead. The other electronics (such as aprocessor, a battery, and/or a wireless communication module) is locatedinside the box 150, which also holds the cameras 148 and 149.

FIG. 33a and FIG. 33b illustrate two right and left clip-on devices 160and 161, respectively, configured to be attached behind an eyeglassesframe 165. The clip-on device 160 includes an inward-facing head-mountedcamera 162 pointed at a region on the lower part of the face (such asthe upper lip, mouth, nose, and/or cheek), an inward-facing head-mountedcamera 163 pointed at the forehead, and other electronics 164 (such as aprocessor, a battery, and/or a wireless communication module). Theclip-on devices 160 and 161 may include additional cameras illustratedin the drawings as black circles.

FIG. 32a and FIG. 32b illustrate a single-unit clip-on device 170,configured to be attached behind an eyeglasses frame 176. Thesingle-unit clip-on device 170 includes inward-facing head-mountedcameras 171 and 172 pointed at regions on the lower part of the face(such as the upper lip, mouth, nose, and/or cheek), inward-facinghead-mounted cameras 173 and 174 pointed at the forehead, a spring 175configured to apply force that holds the clip-on device 170 to the frame176, and other electronics 177 (such as a processor, a battery, and/or awireless communication module). The clip-on device 170 may includeadditional cameras illustrated in the drawings as black circles.

FIG. 34 illustrates two right and left clip-on devices 153 and 154,respectively, configured to attached/detached from an eyeglasses frame,and having protruding arms to hold the inward-facing head-mountedcameras. Head-mounted camera 155 captures a region on the lower part ofthe face, head-mounted camera 156 captures a region on the forehead, andthe left clip-on device 154 further includes other electronics 157 (suchas a processor, a battery, and/or a wireless communication module). Theclip-on devices 153 and 154 may include additional cameras illustratedin the drawings as black circles.

It is noted that the elliptic and other shapes of the ROIs in some ofthe drawings are just for illustration purposes, and the actual shapesof the ROIs are usually not as illustrated. It is possible to calculatethe accurate shape of an ROI using various methods, such as acomputerized simulation using a 3D model of the face and a model of ahead-mounted system (HMS) to which a camera is physically coupled, or byplacing a LED instead of the sensor, while maintaining the same field ofview (FOV) and observing the illumination pattern on the face.Furthermore, illustrations and discussions of a camera represent one ormore cameras, where each camera may have the same FOV and/or differentFOVs. Unless indicated to the contrary, the cameras may include one ormore sensing elements (pixels), even when multiple sensing elements donot explicitly appear in the figures; when a camera includes multiplesensing elements then the illustrated ROI usually refers to the totalROI captured by the camera, which is made of multiple regions that arerespectively captured by the different sensing elements. The positionsof the cameras in the figures are just for illustration, and the camerasmay be placed at other positions on the HMS.

Sentences in the form of an “ROI on an area”, such as ROI on theforehead or an ROI on the nose, refer to at least a portion of the area.Depending on the context, and especially when using a camera having asmall number of pixels, the ROI may cover another area (in addition tothe area). For example, a sentence in the form of “an ROI on the nose”may refer to either: 100% of the ROI is on the nose, or some of the ROIis on the nose and some of the ROI is on the upper lip.

Various embodiments described herein involve detections of physiologicalresponses based on user measurements. Some examples of physiologicalresponses include stress, an allergic reaction, an asthma attack, astroke, congestive heart failure, dehydration, intoxication (includingdrunkenness), a headache (which includes a migraine), and/or fatigue.Other examples of physiological responses include manifestations offear, startle, sexual arousal, anxiety, joy, pain or guilt. Still otherexamples of physiological responses include physiological signals suchas cardiovascular parameters (such as heart rate, blood pressure, and/orcardiac output), temperature, values of eye-related parameters (such aseye movements and/or pupil diameter), values of speech relatedparameters (such as frequencies and/or tempo), and/or values ofrespiratory related parameters (such as respiration rate, tidal volume,and/or exhale duration) of the user. Optionally, detecting aphysiological response may involve one or more of the following:determining whether the user has/had the physiological response,identifying an imminent attack associated with the physiologicalresponse, and/or calculating the extent of the physiological response.

In some embodiments, detection of the physiological response is done byprocessing measurements that fall within a certain window of time thatcharacterizes the physiological response. For example, depending on thephysiological response, the window may be five seconds long, thirtyseconds long, two minutes long, five minutes long, fifteen minutes long,or one hour long. Detecting the physiological response may involveanalysis of measurements taken during multiple of the above-describedwindows, such as measurements taken during different days. In someembodiments, a computer may receive a stream of measurements, takenwhile the user wears an HMS with coupled cameras and/or other sensorsduring the day, and periodically evaluate measurements that fall withina sliding window of a certain size.

In some embodiments, models are generated based on measurements takenover long periods. Sentences of the form of “measurements taken duringdifferent days” or “measurements taken over more than a week” are notlimited to continuous measurements spanning the different days or overthe week, respectively. For example, “measurements taken over more thana week” may be taken by eyeglasses equipped with cameras and/or othersensors, which are worn for more than a week, 8 hours a day. In thisexample, the user is not required to wear the eyeglasses while sleepingin order to take measurements over more than a week. Similarly,sentences of the form of “measurements taken over more than 5 days, atleast 2 hours a day” refer to a set comprising at least 10 measurementstaken over 5 different days, where at least two measurements are takeneach day at times separated by at least two hours.

Utilizing measurements taken over a long period (e.g., measurementstaken on “different days”) may have an advantage, in some embodiments,of contributing to the generalizability of a trained model. Measurementstaken over the long period likely include measurements taken indifferent environments, and/or measurements taken while the measureduser was in various physiological and/or mental states (e.g.,before/after meals, and/or while the measured user wassleepy/energetic/happy/depressed, etc.). Training a model on such datacan improve the performance of systems that utilize the model in thediverse settings often encountered in real-world use (as opposed tocontrolled laboratory-like settings). Additionally, taking themeasurements over the long period may have the advantage of enablingcollection of a large amount of training data that is required for somemachine learning approaches (e.g., “deep learning”).

Detecting the physiological response may involve performing varioustypes of calculations by a computer. Optionally, detecting thephysiological response may involve performing one or more of thefollowing operations: comparing measurements to a threshold (when thethreshold is reached that may be indicative of an occurrence of thephysiological response), comparing measurements to a reference timeseries, and/or by performing calculations that involve a model trainedusing machine learning methods. Optionally, the measurements upon whichthe one or more operations are performed are taken during a window oftime of a certain length, which may optionally depend on the type ofphysiological response being detected. In one example, the window may beshorter than one or more of the following durations: five seconds,fifteen seconds, one minute, five minutes, thirty minutes, one hour,four hours, one day, or one week. In another example, the window may belonger than one or more of the aforementioned durations. Thus, whenmeasurements are taken over a long period, such as measurements takenover a period of more than a week, detection of the physiologicalresponse at a certain time may be done based on a subset of themeasurements that falls within a certain window near the certain time;the detection at the certain time does not necessarily involve utilizingall values collected throughout the long period.

In some embodiments, detecting the physiological response of a user mayinvolve utilizing baseline measurement values, most of which were takenwhen the user was not experiencing the physiological response.Optionally, detecting the physiological response may rely on observing achange to typical measurement value at one or more ROIs (the baseline),where different users might have different typical measurement values atthe ROIs (i.e., different baselines). Optionally, detecting thephysiological response may rely on observing a change to a baselinelevel, which is determined based on previous measurements taken duringthe preceding minutes and/or hours.

In some embodiments, detecting a physiological response involvesdetermining the extent of the physiological response, which may beexpressed in various ways that are indicative of the extent of thephysiological response, such as: (i) a binary value indicative ofwhether the user experienced, and/or is experiencing, the physiologicalresponse, (ii) a numerical value indicative of the magnitude of thephysiological response, (iii) a categorial value indicative of theseverity/extent of the physiological response, (iv) an expected changein measurements of an ROI, and/or (v) rate of change in measurements ofan ROI. Optionally, when the physiological response corresponds to aphysiological signal (e.g., a heart rate, a breathing rate, or an extentof frontal lobe brain activity), the extent of the physiologicalresponse may be interpreted as the value of the physiological signal.

One approach for detecting a physiological response, which may beutilized in some embodiments, involves comparing measurements of one ormore ROIs to a threshold. In these embodiments, the computer may detectthe physiological response by comparing the measurements, and/or valuesderived therefrom (e.g., a statistic of the measurements and/or afunction of the measurements), to the threshold to determine whether itis reached. Optionally, the threshold may include a threshold in thetime domain, a threshold in the frequency domain, an upper threshold,and/or a lower threshold. When a threshold involves a certain change toa value (such as temperature or heart rate), the certain change may bepositive or negative. Different physiological responses described hereinmay involve different types of thresholds, which may be an upperthreshold (where reaching the threshold means≥the threshold) or a lowerthreshold (where reaching the threshold means≤the threshold).

Another approach for detecting a physiological response, which may beutilized in some embodiments, may be applicable when the measurements ofa user are treated as time series data. In some embodiments, thecomputer may compare measurements (represented as a time series) to oneor more reference time series that correspond to periods of time inwhich the physiological response occurred. Additionally oralternatively, the computer may compare the measurements to otherreference time series corresponding to times in which the physiologicalresponse did not occur. Optionally, if the similarity between themeasurements and a reference time series corresponding to aphysiological response reaches a threshold, this is indicative of thefact that the measurements correspond to a period of time during whichthe user had the physiological response. Optionally, if the similaritybetween the measurements and a reference time series that does notcorrespond to a physiological response reaches another threshold, thisis indicative of the fact that the measurements correspond to a periodof time in which the user did not have the physiological response. Timeseries analysis may involve various forms of processing involvingsegmenting data, aligning data, clustering, time warping, and variousfunctions for determining similarity between sequences of time seriesdata. Some of the techniques that may be utilized in various embodimentsare described in Ding, Hui, et al. “Querying and mining of time seriesdata: experimental comparison of representations and distance measures.”Proceedings of the VLDB Endowment 1.2 (2008): 1542-1552, and in Wang,Xiaoyue, et al. “Experimental comparison of representation methods anddistance measures for time series data.” Data Mining and KnowledgeDiscovery 26.2 (2013): 275-309.

Herein, “machine learning” methods refers to learning from examplesusing one or more approaches. Optionally, the approaches may beconsidered supervised, semi-supervised, and/or unsupervised methods.Examples of machine learning approaches include: decision tree learning,association rule learning, regression models, nearest neighborsclassifiers, artificial neural networks, deep learning, inductive logicprogramming, support vector machines, clustering, Bayesian networks,reinforcement learning, representation learning, similarity and metriclearning, sparse dictionary learning, genetic algorithms, rule-basedmachine learning, and/or learning classifier systems.

Herein, a “machine learning-based model” is a model trained usingmachine learning methods. For brevity's sake, at times, a “machinelearning-based model” may simply be called a “model”. Referring to amodel as being “machine learning-based” is intended to indicate that themodel is trained using machine learning methods (otherwise, “model” mayalso refer to a model generated by methods other than machine learning).

In some embodiments, which involve utilizing a machine learning-basedmodel, a computer is configured to detect the physiological response bygenerating feature values based on the measurements (and possibly othervalues), and/or based on values derived therefrom (e.g., statistics ofthe measurements). The computer then utilizes the machine learning-basedmodel to calculate, based on the feature values, a value that isindicative of whether, and/or to what extent, the user is experiencing(and/or is about to experience) the physiological response. Optionally,calculating said value is considered “detecting the physiologicalresponse”. Optionally, the value calculated by the computer isindicative of the probability that the user has/had the physiologicalresponse.

Herein, feature values may be considered input to a computer thatutilizes a model to perform the calculation of a value, such as thevalue indicative of the extent of the physiological response mentionedabove. It is to be noted that the terms “feature” and “feature value”may be used interchangeably when the context of their use is clear.However, a “feature” typically refers to a certain type of value, andrepresents a property, while “feature value” is the value of theproperty with a certain instance (sample). For example, a feature may betemperature at a certain ROI, while the feature value corresponding tothat feature may be 36.9° C. in one instance and 37.3° C. in anotherinstance.

In some embodiments, a machine learning-based model used to detect aphysiological response is trained based on data that includes samples.Each sample includes feature values and a label. The feature values mayinclude various types of values. At least some of the feature values ofa sample are generated based on measurements of a user taken during acertain period of time. Optionally, some of the feature values may bebased on various other sources of information described herein. Thelabel is indicative of a physiological response of the usercorresponding to the certain period of time. Optionally, the label maybe indicative of whether the physiological response occurred during thecertain period, and/or the extent of the physiological response duringthe certain period. Additionally or alternatively, the label may beindicative of how long the physiological response lasted. Labels ofsamples may be generated using various approaches, such as self-reportby users, annotation by experts that analyze the training data,automatic annotation by a computer that analyzes the training dataand/or analyzes additional data related to the training data, and/orutilizing additional sensors that provide data useful for generating thelabels. It is to be noted that herein when it is stated that a model istrained based on certain measurements, it means that the model wastrained on samples comprising feature values generated based on thecertain measurements and labels corresponding to the certainmeasurements. Optionally, a label corresponding to a measurement isindicative of the physiological response at the time the measurement wastaken.

Various types of feature values may be generated based on measurementsand/or changes to the measurements. In order to better detectphysiological responses that take some time to manifest, in someembodiments, some feature values may describe measurements at a certainROI at different points of time. Optionally, these feature values mayinclude various functions and/or statistics of the measurements such asminimum/maximum measurement values and/or average values during certainwindows of time.

It is to be noted that when it is stated that feature values aregenerated based on data comprising multiple sources, it means that foreach source, there is at least one feature value that is generated basedon that source (and possibly other data). Optionally, a sample isconsidered generated based on measurements of a user when it includesfeature values generated based on the measurements of the user.

In addition to feature values that are generated based on measurements,in some embodiments, at least some feature values utilized by a computer(e.g., to detect a physiological response or train a mode) may begenerated based on additional sources of data that may affect the user'smeasurements. Some examples of the additional sources include: (i)measurements of the environment such as temperature, humidity level,noise level, elevation, air quality, a wind speed, precipitation, andinfrared radiation; (ii) contextual information such as the time of day(e.g., to account for effects of the circadian rhythm), day of month(e.g., to account for effects of the lunar rhythm), day in the year(e.g., to account for seasonal effects), and/or stage in a menstrualcycle; and/or (iii) information about the user being measured such assex, age, weight, height, and/or body build. It is noted that thefeature values may be generated based on physiological signals of theuser obtained by one or more sensors, such as a visible-light camera, athermal camera, a microphone, a head-mounted accelerometer, aneye-tracker, a photoplethysmogram (PPG) sensor, an electrocardiogram(ECG) sensor, an electroencephalography (EEG) sensor, a galvanic skinresponse (GSR) sensor, and/or a thermistor.

The machine learning-based model used to detect a physiological responsemay be trained, in some embodiments, based on data collected inday-to-day, real world scenarios. As such, the data may be collected atdifferent times of the day, while users perform various activities, andin various environmental conditions. Utilizing such diverse trainingdata may enable a trained model to be more resilient to the variouseffects different conditions can have on the values of the usermeasurements, and consequently, be able to achieve better detection ofthe physiological response in real world day-to-day scenarios.

Since real world day-to-day conditions are not the same all the time,sometimes detection of the physiological response may be hampered bywhat is referred to herein as “confounding factors”. Some examples ofconfounding factors include: (i) environmental phenomena such as directsunlight, air conditioning, and/or wind; (ii) things that are on theuser's face, which are not typically there and/or do not characterizethe faces of most users (e.g., cosmetics, ointments, sweat, hair, facialhair, skin blemishes, acne, inflammation, piercings, body paint, andfood leftovers); (iii) physical activity that may affect the user'sheart rate, blood circulation, and/or blood distribution (e.g., walking,running, jumping, and/or bending over); (iv) consumption of substancesto which the body has a physiological response, such as variousmedications, alcohol, caffeine, tobacco, and/or certain types of food;and/or (v) disruptive facial movements (e.g., frowning, talking, eating,drinking, sneezing, and coughing).

Occurrences of confounding factors may not always be easily identifiedin the measurements. Thus, in some embodiments, systems may incorporatemeasures designed to accommodate for the confounding factors. In someembodiments, these measures may involve generating feature values thatare based on additional sensors, other than the sensor affected gy theconfounding factors. In some embodiments, these measures may involverefraining from detecting the physiological response, which should beinterpreted as refraining from providing an indication that the user hasthe physiological response.

Training data used to train a model for detecting a physiologicalresponse may include, in some embodiments, a diverse set of samplescorresponding to various conditions, some of which involve occurrence ofconfounding factors (when there is no physiological response, and/orwhen there is a physiological response). Having samples in which aconfounding factor occurs can lead to a model that is less susceptibleto wrongfully detect the physiological response (which may be consideredan occurrence of a false positive) in real world situations.

When a model is trained with training data comprising samples generatedfrom measurements of multiple users, the model may be considered ageneral model. When a model is trained with training data comprising atleast a certain proportion of samples generated from measurements of acertain user, and/or when the samples generated from the measurements ofthe certain user are associated with at least a certain proportion ofweight in the training data, the model may be considered a personalizedmodel for the certain user. Optionally, the personalized model for thecertain user provides better results for the certain user, compared to ageneral model that was not personalized for the certain user.Optionally, personalized model may be trained based on measurements ofthe certain user, which were taken while the certain user was indifferent situations; for example, train the model based on measurementstaken while the certain user had a headache/epilepsy/stress/angerattack, and while the certain user did not have said attack.Additionally or alternatively, the personalized model may be trainedbased on measurements of the certain user, which were taken over aduration long enough to span different situations; examples of such longenough durations may include: a week, a month, six months, a year, andthree years.

Training a model that is personalized for a certain user may requirecollecting a sufficient number of training samples that are generatedbased on measurements of the certain user. Thus, initially detecting thephysiological response with the certain user may be done utilizing ageneral model, which may be replaced by a personalized model for thecertain user, as a sufficiently large number of samples are generatedbased on measurements of the certain user. Another approach involvesgradually modifying a general model based on samples of the certain userin order to obtain the personalized model.

After a model is trained, the model may be provided for use by a systemthat detects the physiological response. Providing the model may involveperforming different operations. In one embodiment, providing the modelto the system involves forwarding the model to the system via a computernetwork and/or a shared computer storage medium (e.g., writing the modelto a memory that may be accessed by the system that detects thephysiological response). In another embodiment, providing the model tothe system involves storing the model in a location from which thesystem can retrieve the model, such as a database and/or a cloud-basedstorage from which the system may retrieve the model. In still anotherembodiment, providing the model involves notifying the system regardingthe existence of the model and/or regarding an update to the model.Optionally, this notification includes information needed in order forthe system to obtain the model.

A model for detecting a physiological response may include differenttypes of parameters. Following are some examples of variouspossibilities for the model and the type of calculations that may beaccordingly performed by a computer in order to detect the physiologicalresponse: (a) the model comprises parameters of a decision tree.Optionally, the computer simulates a traversal along a path in thedecision tree, determining which branches to take based on the featurevalues. A value indicative of the physiological response may be obtainedat the leaf node and/or based on calculations involving values on nodesand/or edges along the path; (b) the model comprises parameters of aregression model (e.g., regression coefficients in a linear regressionmodel or a logistic regression model). Optionally, the computermultiplies the feature values (which may be considered a regressor) withthe parameters of the regression model in order to obtain the valueindicative of the physiological response; and/or (c) the model comprisesparameters of a neural network. For example, the parameters may includevalues defining at least the following: (i) an interconnection patternbetween different layers of neurons, (ii) weights of theinterconnections, and (iii) activation functions that convert eachneuron's weighted input to its output activation. Optionally, thecomputer provides the feature values as inputs to the neural network,computes the values of the various activation functions and propagatesvalues between layers, and obtains an output from the network, which isthe value indicative of the physiological response.

A user interface (UI) may be utilized, in some embodiments, to notifythe user and/or some other entity, such as a caregiver, about thephysiological response, and/or present an alert responsive to anindication that the extent of the physiological response reaches athreshold. The UI may include a screen to display the notificationand/or alert, a speaker to play an audio notification, a tactile UI,and/or a vibrating UI. In some embodiments, “alerting” about aphysiological response of a user refers to informing about one or moreof the following non-limiting examples: the occurrence of aphysiological response that the user does not usually have (e.g., astroke, intoxication, and/or dehydration), an imminent physiologicalresponse (e.g., an allergic reaction, an epilepsy attack, and/or amigraine), and an extent of the physiological response reaching athreshold (e.g., stress and/or blood pressure reaching a predeterminedlevel).

Due to the mostly symmetric nature of the human body, when the faceundergoes temperature changes, e.g., due to external factors such as thetemperature in the environment or internal factors such as anactivity-related rise in body temperature, the changes to the face aregenerally symmetric. That is, the temperature changes at a region ofinterest (ROI) on the left side of the face (e.g., the left side of theforehead) are usually similar to the temperature changes at thesymmetric ROI on the right side of the face (e.g., the right side of theforehead). However, when the temperature on the face changes in anasymmetric way, this can be indicative of various physiologicalresponses and/or undesirable phenomena. Some examples of phenomena thatmay be identified by detecting asymmetric thermal patterns (“thermalasymmetry”) on a user's face include some types of strokes. In the caseof stroke, often the decreased blood flow in certain regions of the head(due to the stroke) can cause a decrease in the cutaneous temperaturesnear those certain regions.

Some embodiments utilize head-mounted sensors in order to detect strokesigns that involve detectable changes in temperature and/or blood flowdue to a stroke event.

In some embodiments, “stroke symptoms” refers to changes tofunction/sensation reported by the patient. In some embodiments, “strokesigns” refers to objective changes observable by a human individualand/or a sensor. Detecting stroke symptoms and/or stroke signs may alsobe referred to herein as detecting “whether the user has suffered from astroke”. It is to be noted that a detection that the user has sufferedfrom a stroke may be interpreted, in some cases, as indicating thatthere is a higher than normal risk that the user has suffered from astroke, and that certain actions should be taken (such as furtherinvestigation of the user's state by other means or seeking medicalattention). Thus, in some embodiments, detection by the computer thatthe user has suffered from a stroke may serve as an initial step thattriggers further steps for diagnosing the user's condition and not adefinitive final step in diagnosing the user's state.

In some embodiments, a system configured to detect a stroke based onthermal measurements includes at least one inward-facing head-mountedthermal camera (CAM) and computer. The at least one CAM is/areconfigured to take thermal measurements of at least first and secondregions on the right and left sides of the head (TH_(R1) and TH_(L1),respectively) of a user. Optionally, the at least one CAM is locatedbelow the first and second regions, and does not occlude the first andsecond regions. The computer is configured to detect, based on TH_(R1)and TH_(L1), whether the user has suffered a stroke. Optionally,detecting whether the user has suffered from a stroke refers to a recentstroke event, such as an ischemic or hemorrhagic stroke event thatstarted a short while before TH_(R1) and TH_(L) were taken, where “ashort while” may be a period between minutes and several hours beforeTH_(R1) and TH_(L1) were taken. In this scenario, detection that theuser has suffered from a stroke may enable an intervention that canreduce the permanent damage of the stroke. Additionally detectingwhether the user has suffered from a stroke may also refer, in someembodiments, to earlier stroke events, which occurred more than sixhours before TH_(R1) and TH_(L1) were taken.

The first and second regions of which measurements are taken may includeportions of various parts of the head. For example, in differentembodiments, the first and second regions may cover a portion of atleast one of the following pairs of regions: the right and left sides ofthe forehead, the right and left temples, the right and left cheeks, theright and left earlobes, behind the right and left ears, periorbitalareas around the right and left eyes, the area of the right and leftmastoid processes.

In one embodiment, each of the at least one CAM is physically coupled toa frame worn on a user's head, and is located less than 15 cm, 5 cm, or2 cm from the user's face. In another embodiment, the at least one CAMis physically coupled to a clip-on, and the clip-on comprises a bodyconfigured to be attached and detached, multiple times, from a frameconfigured to be worn on the user's head. FIG. 30a to FIG. 34 illustratevarious examples of embodiments of systems that include a clip-on whichmay have the at least one CAM coupled thereto.

In one embodiment, due to the angle between the optical axis of acertain CAM from among the at least one CAM and the Frankfort horizontalplane, the Scheimpflug principle may be employed in order to capturesharper images with the certain CAM. For example, when the user wears aframe to which the certain CAM is coupled, the certain CAM has a certaintilt greater than 20 between its sensor and lens planes, in order tocapture the sharper images. Additional details regarding application ofthe Scheimpflug are provided herein further below.

The at least one CAM may be a single CAM, in one embodiment, such as asingle FPA that captures images that include a portion of both sides ofthe forehead. In this embodiment, TH_(R1) and TH_(L1) includemeasurements that cover portions of the left side and right side ofuser's forehead, respectively. Optionally, TH_(R1) and TH_(L1) may bemeasured by different subsets of pixels of the FPA. An example of asingle thermal camera that may be utilized to measure TH_(R1) andTH_(L1) is camera 149 illustrated in FIG. 31a , which in this example,is an inward-facing head-mounted thermal camera. Another example, of asystem that includes a single thermal camera is illustrated in FIG. 3,which illustrates a user wearing glasses with a single thermal sensor605 that measures ROIs 606 a and 606 b on the right and left of theforehead, respectively. The user in the figure has already been affectedby a stroke, as is evident by the lower temperature detected on the leftside of the forehead and the drooping of the left side of the face.

In other embodiments, the at least one CAM may include two or more CAMs.Optionally, the at least one CAM includes two CAMs, denoted CAM1 andCAM2. Optionally, CAM1 and CAM2 are located at least 0.5 cm to the rightand to the left of the vertical symmetry axis that divides the face,respectively. Optionally, each of CAM1 and CAM2 weighs below 10 g, 5 g,or 1 g.

FIG. 4 illustrates one example of a system for detecting a stroke thatincludes at least CAM1 and CAM2 described above. The figure illustratesa user wearing a frame with CAM1 and CAM2 (562 and 563, respectively)coupled thereto, which measure ROIs on the right and left cheeks (ROIs560 and 561, respectively). The measurements indicate that the left sideof the face is colder than the right side of the face. Based on thesemeasurements, and possibly additional data, the system detects thestroke and issues an alert. Optionally, the user's facial expression isslightly distorted and asymmetric, and a video camera providesadditional data in the form of images that may help in detecting thestroke.

FIG. 22 to FIG. 25 illustrate HMSs that may be used to detect a strokethat include two or more CAMs. FIG. 22 illustrates inward-facinghead-mounted cameras 30 and 31 that measure regions 32 and 33 on theforehead, respectively. FIG. 25 illustrates (i) inward-facinghead-mounted cameras 91 and 92 that are mounted to protruding arms andmeasure regions 97 and 98 on the forehead, respectively, (ii)inward-facing head-mounted cameras 95 and 96, which are also mounted toprotruding arms, which measure regions 101 and 102 on the lower part ofthe face, respectively, and (iii) head-mounted cameras 93 and 94 thatmeasure regions on the periorbital areas 99 and 100, respectively.

Depending on the locations the at least first and second regions on theright and left sides of the head, CAM1 and CAM2 mentioned above may belocated in specific locations with respect to the face. In one example,CAM1 and CAM2 are located outside the exhale stream of the mouth and/orthe exhale streams of the nostrils. In another example, each of CAM1 andCAM2 is located less than 10 cm from the face and there are anglesgreater than 200 between the Frankfort horizontal plane and the opticalaxes of CAM1 and CAM2.

Measurements of additional regions on the head may be used to detectwhether the user suffered from a stroke. In one embodiment, the at leastone CAM is further configured to take thermal measurements of at leastthird and fourth regions on the right and left sides of the head(TH_(R2) and TH_(L2), respectively), and the computer is furtherconfigured to detect whether the user has suffered a stroke also basedon TH_(R2) and TH_(L2) (in addition to TH_(R1) and TH_(L1)). Optionally,the middles of the first and second regions are at least 1 cm above themiddles of the third and fourth regions, respectively. Optionally, thethird and fourth regions cover at least a portion of one of thefollowing pairs of regions (which is not covered by the first and secondregions): the right and left sides of the forehead, the right and lefttemples, the right and left cheeks, the right and left earlobes, behindthe right and left ears, periorbital areas around the right and lefteyes, the area of the right and left mastoid processes.

In one embodiment, the system optionally includes at least oneoutward-facing head-mounted thermal camera (CAM_(out)), which isconfigured to take thermal measurements of the environment (TH_(ENV)).Optionally, the computer is further configured to also utilize TH_(EN)V(in addition to TH_(R1) and TH_(L1)) to detect whether the user hassuffered a stroke.

There are various approaches that may be used by the computer, indifferent embodiments, to detect based on TH_(R1) and TH_(L1) whetherthe user has suffered a stroke. In some embodiments, detecting whetherthe user has suffered a stroke involves comparing TH_(R1) and TH_(L1)(referred to as current measurements) to previously taken thermalmeasurements (previous measurements), such as previously taken TH_(R1)and TH_(L1) of the user. Optionally, the previous measurements are takenat least fifteen minutes before the current measurements. Optionally,the previous measurements are taken at least one hour before the currentmeasurements. Optionally, the previous measurements are taken at leastone six hours before the current measurements. Optionally, the previousmeasurements are taken at least one day before the current measurements.

In some embodiments, the previous measurements are taken over a longperiod and are used to calculate baseline thermal values of regions ofthe face, and/or used to generate various models, which are used todetect a stroke, as discussed below. It is to be noted that discussionbelow regarding detection of the stroke based on TH_(R1) and TH_(L1) canbe generalized to involve thermal measurements of other regions (such asTH_(R2) and TH_(L2) discussed above).

In one embodiment, the computer calculates a magnitude of thermalasymmetry of the head of the user based on a difference between TH_(R1)and TH_(L1), and compares the magnitude to a threshold. Responsive tothe magnitude reaching the threshold, the computer detects that the userhas suffered a stroke. Optionally, the difference between TH_(R1) andTH_(L1) needs to have reached the threshold (i.e., equal the thresholdvalue or exceed it) for at least a predetermined minimal duration, suchas at least one minute, at least five minutes, at least fifteen minutes,or at least some other period that is greater than 30 minutes.Optionally, the threshold is calculated based on previous magnitudes ofthermal asymmetry of the head of the user, which were calculated basedon previously taken TH_(R1) and TH_(L1) of the user. Thus, thisthreshold may be set according to a baseline thermal asymmetry thatrepresents the typical difference in the temperatures of the first andsecond regions; a stroke may be detected when the thermal asymmetrybecomes significantly different from this baseline.

In another embodiment, the computer calculates feature values andutilizes a model to calculate, based on the feature values, a valueindicative of whether the user has suffered a stroke. At least some ofthe feature values are generated based on TH_(R1) and TH_(L1) of theuser; examples of feature values that may be generated are given in thediscussion regarding feature values that may be generated to detect aphysiological response (herein suffering from a stroke is considered atype of physiological response). Optionally, at least some featurevalues are generated based on additional sources of information (otherthan the at least one CAM), such as additional thermal cameras,additional sensors that measure physiological signals of the user (e.g.,heart rate or galvanic skin response), and/or additional sensors thatmeasure the environment. Optionally, one or more of the feature valuesare indicative of the extent of difference between TH_(R1) and TH_(L1)of the user and previous TH_(R1) and TH_(L1) of the user, taken at leasta certain period earlier (such as at least fifteen minutes earlier, onehour earlier, a day earlier, or more than a day earlier). Thus, theseone or more feature values may represent a difference between thecurrent thermal measurements and baseline thermal values for the firstand second regions. Optionally, the model is generated based on datathat includes previously taken TH_(R1) and TH_(L1) of the user and/orother users. Optionally, when data includes previously taken TH_(R1) andTH_(L1) of other users, at least some of the measurements of the otherusers were taken while they did not suffer from a stroke, and at leastsome of the measurements of the other users were taken while they didsuffer from a stroke.

In another embodiment, the computer calculates a value indicative of ajoint probability of TH_(R1) and TH_(L1) based on a model that includesdistribution parameters calculated based on previously taken TH_(R1) andTH_(L1) of the user. Thus, the model describes a typical distribution ofthermal measurements on regions of the user's head (when the user didnot suffer from a stroke). The computer may compare the value indicativeof the joint probability to a threshold, and responsive to the valuebeing below the threshold, the computer detects that the user hassuffered a stroke. The threshold may represent an atypical thermalasymmetry, with very low probability to normally occur, which warrantsan alert of the possibility that the user has had a stroke.

In some embodiments described herein, detecting whether the user hassuffered a stroke may involve calculating a change to thermal asymmetryon the head based on a change between thermal measurements taken atdifferent times. This calculation can be performed in different ways, asdescribed below.

In one embodiment, the computer calculates the change between thethermal measurements as follows: calculate a temperature differencebetween the first and second regions at time x (ΔT_(x)) based on[TH_(R1) and TH_(L1)] taken at time x, calculate a temperaturedifference between the first and second regions at time y (ΔT_(y)) basedon [TH_(R1) and TH_(L1)] taken at time y, and calculate the outputindicative of the change in the thermal asymmetry on the face based on adifference between ΔT_(x) and ΔT_(y).

The embodiment described above may optionally be implemented using adifferential amplifier that receives TH_(R1) and TH_(L1) as inputs, andoutputs the temperature difference between the first and second regions.Optionally, the at least one CAM is/are based on thermopile sensors.Alternatively, the at least one CAM is/are based on pyroelectricsensors. In one example, pairs of thermal sensor elements are wired asopposite inputs to a differential amplifier in order for the thermalmeasurements to cancel each other and thereby remove the averagetemperature of the field of view from the electrical signal. This allowsthe at least one CAM to be less prone to providing false indications oftemperature changes in the event of being exposed to brief flashes ofradiation or field-wide illumination. This embodiment may also minimizecommon-mode interference, and as a result improve the accuracy of thethermal cameras.

In another embodiment, the computer calculates the change between thethermal measurements as follows: the computer calculates a temperaturechange between TH_(R1) taken at times t₁ and t₂ (ΔTH_(R1)), calculates atemperature change between TH_(L1) taken at times t₁ and t₂ (ΔTH_(L1)),and then calculates the output indicative of the thermal asymmetry onthe face based on a difference between ΔTH_(R1) and ΔTH_(L1).

It is noted that sentences such as “calculate a difference between X andY” or “detect a difference between X and Y” may be achieved by anyfunction that is proportional to the difference between X and Y.

The detection of a stroke based on TH_(R1) on TH_(L1), and optionallyother inputs, such as additional thermal measurements or additionalphysiological signals, may be sufficient, in some embodiments, to promptthe computer to take an action such as alerting the user, a caregiver,and/or emergency services. In other embodiments, the detection may beconsidered an indication that there is a risk that the user has suffereda stroke, and the computer may encourage the user to take a test (e.g.,the FAST test described below or portions thereof), in order to validatethe detection. Optionally, measurements and/or results taken during thetest may be used in addition to TH_(R1) and TH_(L1) and the optionaladditional inputs, or instead of that data, in order to make a second,more accurate detection of whether the user has suffered from a stroke.

Upon detecting that the user has suffered from a stroke, in someembodiments, the computer may prompt the user to take a test to validateand/or increase the confidence in the detection. This test may involveperforming one or more steps of a FAST test. Herein, FAST is an acronymused as a mnemonic to help detect and enhance responsiveness to theneeds of a person having a stroke. The acronym stands for Facialdrooping, Arm weakness, Speech difficulties and Time to call emergencyservices. Facial drooping involves a section of the face, usually onlyon one side, that is drooping and hard to move (often recognized by acrooked smile). Arm weakness typically involves an inability to raiseone's arm fully. Speech difficulties typically involve an inability ordifficulty to understand or produce speech.

FIG. 5 to FIG. 8 illustrate physiological and behavioral changes thatmay occur following a stroke, which may be detected using embodimentsdescribed herein. FIG. 5 illustrates a person at time zero, at the verybeginning of the onset of the stroke. At this time there may be nodetectable signs of the stroke. FIG. 6 illustrates the person's stateafter 5 minutes since the beginning of the stroke. By this time, certainchanges to the blood flow in the head, which were caused by the stroke,may be detectable. For example, the blood flow on one side of the facemay decrease. FIG. 7 illustrates the person's state 15 minutes after thebeginning of the stroke. At this time, temperature changes to regions ofthe face, which are due to the changes in blood flow, may be detectable.FIG. 8 illustrates the person's state 45 minutes after the beginning ofthe stroke. At this time, droopiness of one side of the face (e.g., dueto reduced blood flow and impaired neurologic control) may beobservable.

The following are some examples of steps that may be taken to furtherdiagnose whether the user has suffered from a stroke (e.g., steps from aFAST test). Optionally, results of these steps may be used to furtherstrengthen the computer's detection (e.g., to increase confidence in thedetection) or change the detection (e.g., and decide the detection was afalse alarm). Optionally, upon determining, based on one or more of thesteps, that the user may have a suffered a stroke, the computer performsat least one of the following steps: (i) instruct the user to seekemergency medical assistance, (ii) connect the user through live videochat with a medical specialist, and (iii) automatically alert apredetermined person and/or entity about the user's condition.

In one embodiment, a camera (e.g., a cellphone camera or aninward-facing camera coupled to a frame worn by the user) takes imagesof at least a portion of the user's face. Optionally, the computer isfurther configured to (i) instruct the user, via a user interface, tosmile and/or stick out the tongue, and (ii) detect, based on analysis ofthe images taken by the camera, whether a portion of one side of theuser's face droops and/or whether the user was able to stick out thetongue. Detecting that the portion of the one side of the user's facedroops may be done based on a comparison of the images of at least aportion of the user's face to previously taken images of at least theportion of the user's face, which were taken at least one hour before.Optionally, the computer utilizes a machine learning based model todetermine whether the comparison indicates that the face droops. In thiscase, the model may be trained based on sets of images that includebefore and after images of people who suffered a stroke. Additionally oralternatively, detecting that the portion of the one side of the user'sface droops is done using a machine learning-based model trained onexamples of images of faces of people with a portion of one side of theface drooping. Optionally, detecting whether the tongue is sticking outmay be done using image analysis and/or machine learning approaches(e.g., utilizing a model trained on previous images of the user and/orother users sticking out their tongue). FIG. 9 illustrates a user who isrequested by a smartphone app to smile. Images of the user may be takenby the smartphone and analyzed in order to determine whether the userhas smiled and/or to what extent the smile is considered “normal”.Similarly, FIG. 10 illustrates a user who is requested by a smartphoneapp to stick out his tongue.

In another embodiment, a microphone is used to record the user's speech.The computer is configured to (i) instruct the user, via a userinterface, to speak (e.g., say a predetermined phrased), and (ii)detect, based on analysis of a recording of the user taken by themicrophone, whether the user's speech is slurred, and/or whether adifference between the recording and previous recordings of the userexceeds a threshold that indicates excessively slurred or difficult.Optionally, the computer uses a model trained based on examples ofpeoples' normal and incoherent speech (e.g., recordings of people beforeand after they had a stroke). FIG. 11 illustrates a user who isrequested by a smartphone app to say a sentence. The user's speech canbe recorded by the smartphone and analyzed to detect slurry and/oruncharacteristic speech.

In yet another embodiment, a sensor is used to take measurementsindicative of at least one of movement and position, of at least one ofthe user's arms. Optionally, the computer is further configured to (i)instruct the user, via a user interface, to raise at least one the arms,and (ii) detect, based on analysis of the measurements taken by thesensor, whether an arm of the user drifts downward. In one example, thesensor is in a cellphone held by the user. In another example, thesensor is in a smartwatch or bracelet on the user's wrist. In stillanother example, the sensor is embedded in a garment worn by the user.FIG. 12 illustrates a user who is requested by a smartphone app to raisehis arms. The user's movements can be detected by the smartphone inorder to determine whether one arm falls. In the illustrated example,the user may be requested to raise the arms at least twice, each timeholding the phone in a different hand. Such measurements can serve as abaseline to compare the rate of ascent and descent of each of the arms.

In still another embodiment, a sensor is used to take measurementsindicative of how the user walks. Optionally, the computer is furtherconfigured to (i) instruct the user, via a user interface, to walk, and(ii) detect, based on analysis of the measurements taken by the sensor,whether a difference between the measurements taken by the sensor andprevious measurements of the user taken by the sensor exceeds athreshold. Optionally, exceeding the threshold is indicative of a suddenloss of balance or coordination. In one example, the sensor is coupledto a frame worn on the user's head (e.g., a glasses frame). In anotherexample, the sensor is in a cellphone, smartwatch, bracelet, garment, orshoe worn by the user.

Early detection of a stroke, e.g., using one or more of the approachesdescribed above, can be essential for minimizing the stroke-relateddamage. With strokes, it is certainly the case that time is of theessence. Early administration of treatment (e.g., within a few hours)can in many cases lead to minimal long term stroke-related damage.However, following a certain window of time (e.g., six hours since thestart of the stroke), severe and irreversible long term damage mayoccur; impacting both quality of life and cost of care. FIG. 13 and FIG.14 illustrate the difference between a timely intervention andintervention that comes too late. In FIG. 13, a patient arrives a shortwhile (about an hour and fifteen minutes) after beginning of a stroke.This patient has moderate stroke damage (denoted by reference numeral655). Intervention at this early stage is mostly successful, and aftertwo months the patient has only slight long term damage (denoted byreference numeral 656), which involves a relatively small impact on thequality of life and a relatively low cost in terms of future medicalexpenses. FIG. 14 illustrates a case in which the patient arrives toolate (e.g., six hours after the start of the stroke). In this scenario,the patient already has more severe stroke damage (denoted by referencenumeral 657). Intervention at this late stage is mostly unsuccessful.Two months later, there is severe long term stroke-related damage, whichinvolves a relatively large impact on the quality of life and arelatively high cost in terms of future medical expenses.

In addition to detecting a stroke, some embodiments may be used todetect an occurrence of a migraine attack. In one embodiment, the firstand second regions are located above the user's eye level, and thecomputer is further configured to detect occurrence of a migraine attackbased on asymmetry between TH_(R1) and TH_(L1). In another embodiment,the first and second regions are located above the user's eye level. Thecomputer is further configured to generate feature values based onTH_(R1) and TH_(L1), and to utilize a model to calculate, based on thefeature values, a value indicative of whether the user is experiencing amigraine attack or whether a migraine attack is imminent. Optionally,the model if generated based on previous TH_(R1) and TH_(L) of the usertaken while the user had a migraine attack or taken 30 minutes or lessbefore the user had a migraine attack.

In some embodiments, a system configured to detect a stroke based onasymmetric changes to blood flow includes at least first and seconddevices and a computer. The first and second devices are located to theright and to the left of the vertical symmetry axis that divides theface of a user, respectively; the first and second devices areconfigured to measure first and second signals (S_(BF1) and S_(BF2),respectively) indicative of blood flow in regions to right and to theleft of the vertical symmetry axis. The computer detects whether theuser has suffered a stroke based on an asymmetric change to blood flow,which is recognizable in S_(BF1) and S_(BF2). Optionally, the systemincludes a frame configured to be worn on a user's head, and the firstand second devices are head-mounted devices that are physically coupledto the right and left sides of the frame, respectively.

The first and second devices may be devices of various types.Optionally, the first and second devices are the same type of device.Alternatively, the first and second devices may be different types ofdevices. The following are some examples of various types of devicesthat can be used in embodiments described herein in order to measure asignal indicative of blood flow in a region of the body of the user.

In one embodiment, the first and second devices comprise first andsecond cameras, respectively. The first and second cameras are based onsensors comprising at least 3×3 pixels configured to detectelectromagnetic radiation having wavelengths in at least a portion ofthe range of 200 nm to 1200 nm. Optionally, the asymmetric changes toblood flow lead to asymmetric facial skin color changes to the face ofthe user. Optionally, the system includes first and second active lightsources configured to illuminate the first and second regions,respectively. In one example, the first and second devices and first andsecond active light sources are head-mounted, and the first and secondactive light sources are configured to illuminate the first and secondregions with electromagnetic radiation having wavelengths in at least aportion of the range of 800 nm to 1200 nm.

In addition to the first and second cameras mentioned above, someembodiments may include first and second outward-facing cameras thattake images of the environment to the right and left of the user's head,respectively. Optionally, the computer utilizes the images to detectwhether the user has suffered a stroke. Optionally, the images of theenvironment are indicative of illumination towards the face, imagestaken by the inward-facing cameras are indicative of reflections fromthe face. Thus, the images of the environment may be used to account, atleast in part, for variations in ambient light that may cause errors indetections of blood flow.

In another embodiment, the first and second devices function as imagingphotoplethysmography devices, and/or the first and second devicesfunction as pulse oximeters.

There are various types of devices that may be used in embodimentsdescribed herein, and ways in which the devices may be attached to theuser and/or located the user's proximity. In one example, the first andsecond device may be head-mounted devices, such as devices physicallycoupled to a frame worn on the user's head (e.g., a frame ofsmartglasses, such as augmented reality glasses, virtual realityglasses, or mixed reality glasses). In another example, the first andsecond devices are embedded in a garment worn by the user (e.g., a smartshirt) or some other wearable accessory (e.g., a necklace, bracelets,etc.). In yet another, the first and second devices may be located inheadrests of a chair in which the user sits. In still another example,the first and second devices are attached to walls (e.g., of a vehiclecabin in which the user sits or a room in which the user spends time).In yet another example, the first and second devices may be coupled torobotic arms. Optionally, the robotic arms may be used to move and/ororient the devices in order to account for the user's movements, whichmay enable the first and second devices to measure the same regions onthe user's head despite the user's movement.

The first and second devices may be utilized to measure various regionson the right and left sides of the user's head. In one embodiment, theregions on the right and left sides include portions of the right andleft temples, respectively. In another embodiment, the regions on theright and left sides include portions of the right and left sides of theforehead, respectively. Optionally, the center of the region on theright is at least 3 cm from the center of the region on the left. Instill another embodiment, the regions on the right and left sidesinclude portions of the right and left cheeks, respectively. In stillanother embodiment, the regions on the right and left sides includeareas behind the right and left ears, respectively. Optionally, theareas behind the right and left ears are below the hairline and cover atleast a portion of the mastoid process in their respective sides of thehead.

FIG. 15a illustrates an embodiment of a system that includes multiplepairs of right and left cameras, and locations on the face that they maybe used to measure. The cameras illustrated in the figure are coupled toa frame worn by the user, which may be, for example, any of the camerasmentioned herein. The illustrated example include: (i) cameras 641 a and641 b that are coupled to the right and left sides of the top of theframe, respectively, which measure ROIs 644 a and 644 b on the right andleft of the forehead, respectively; (ii) cameras 642 a and 642 b thatare coupled to the right and left sides of the bottom of the frame,respectively, which measure ROIs 645 a and 645 b on the right and leftcheeks, respectively; and (iii) cameras 643 a and 643 b that are coupledto the frame near the right and left sides of the nose, respectively,which measure ROIs 646 a and 646 b in the right and left periorbitalregions, respectively.

FIG. 15b illustrates a stroke sign 647, which involves decreased bloodflow in the forehead, and may be detected using the system illustratedin FIG. 15a . Another stroke sign that may be detected by the systemillustrated in FIG. 15c is stroke sign 648, which involves decreasedtemperature in the periorbital region.

FIG. 16a illustrates a system in which the first and second devices arehead-mounted cameras that are located behind the ears. The figureillustrates cameras 651, which may be a downward-facing video camerathat takes images that include portions of the side of the neck. FIG.16b illustrates a variant of the system illustrated in FIG. 16a in whichcamera 652 is a downward-facing camera that includes an extender bodywhich enables the camera to move beyond the hairline in order to obtainunobstructed images of the side of the neck.

FIG. 16c illustrates ischemic stroke 650, which restricts the blood flowto the side of the head (illustrated as patch 653), which may bedetected based on measurements of device 652 in order to alert about theuser having suffered from a stroke.

Various types of values, which are indicative of blood flow (and takenby the first or second devices), can be determined based on a signal,which is denoted S_(BF) in the discussion below. S_(BF) may be, forexample, aforementioned S_(BF1) or S_(BF2). Propagation of a cardiacpulse wave can lead to changes in the blood flow, with increased bloodflow as a result of blood pumped during the systole, and lower bloodflow at other times (e.g., during diastole). The changes in blood flowoften manifest as changes to values in S_(BF). For example, higher bloodflow may correspond to increased intensity of certain colors of pixels(when S_(BF) includes images), temperatures of pixels (when S_(BF)includes thermal images), or increased absorption of certain wavelengths(when S_(BF) includes measurements of a photoplethysmography device).

In some embodiments, calculating a statistic of S_(BF) may provide anindication of the extent of the blood flow. For example, the statisticmay be an average value (e.g., average pixel values), which is acalculated over a period, such as ten seconds, thirty seconds, or aminute. For example, in an embodiment in which the devices are camerasand S_(BF) includes images, the average hue of pixels in the images canbe indicative of the blood flow (e.g., a redder hue may correspond tomore intense blood flow). In an embodiment in which the devices arethermal sensors and S_(BF) includes thermal images, the averagetemperature of pixels in the thermal images can be indicative of theblood flow (e.g., a higher average temperature may correspond to moreintense blood flow).

In other embodiments, analysis of propagation of cardiac pulse waves, asmanifested in the values of S_(BF), can be used to provide indicationsof the extent of blood flow. When S_(BF) includes multiple pixels values(e.g., images or thermal images), propagation of a pulse wave typicallyinvolves an increase in pixel values that correspond to a period of ahigh blood flow during the pulse wave (e.g., when blood flow is drivenby a systole) followed by a decrease in pixel values that correspond toa period of a lower blood flow during the pulse wave (e.g., decreasingtowards a diastolic trough). The speed at which the pulse wavepropagates along a segment of the images of S_(BF) is indicative of thespeed of blood flow in the segment depicted in the images. For example,the speed at which a peak (e.g., corresponding to the systole) is seento propagate in the images is directly correlated with the blood flow.

Another parameter indicative of blood flow that can be derived fromanalysis of a propagation of a pulse wave, as it is manifested in valuesof S_(BF), is the amplitude of the signal. For example, signals thatdisplay a periodic increase and decrease in values of S_(BF), whichcorresponds to the heart rate, can be analyzed to determine thedifference in values (e.g., difference in color, temperature, orintensity) between the peak and the trough observed in the values ofS_(BF) during the transition of a pulse wave. In some embodiments, theamplitude of the signal (e.g., the difference between the value at thepeak and the value at the trough) is correlated with the blood flow. Inone example, the higher the amplitude of the signal, the higher theblood flow.

Another parameter indicative of blood flow that can be derived fromanalysis of a propagation of a pulse wave, as it is manifested in valuesof S_(BF), is the extent to which pixels in images (or thermal images)display a periodic change to their values that reaches a certainthreshold. For example, the area on the face in which the periodicfacial skin color changes (which correspond to the cardiac pulse) reacha certain threshold is correlated with the blood flow. The higher theblood flow, the larger the area.

The extent of Blood flow (e.g., the speed at which the blood flows) iscorrelated with the times at which a pulse wave is detected at differentlocations of the body. Typically, the farther a location from the heart,the later the pulse wave is detected (e.g., detection of a pulse wavemay correspond to the time at which a systolic peak is observed). Thisphenomenon can be utilized, in some embodiments, to calculate valuesindicative of blood flow based on the difference in times at which pulsewaves are detected at different locations. For example, analysis of oneor more S_(BF) signals, which include values that are indicative ofpropagation of a pulse wave at various locations, can determine thedifference in time (Δt) between detection of a pulse wave at differentlocations. The value of Δt is typically correlated with the blood flow:the higher the blood flow, the smaller Δt is expected to be.

The computer is configured, in some embodiments, to detect whether theuser has suffered a stroke based on an asymmetric change to blood flow,which is recognizable in S_(BF1) and S_(BF2).

Herein, sentences of the form “asymmetric change to blood flowrecognizable in S_(BF1) and S_(BF2)” refer to effects of blood flow thatmay be identified and/or utilized by the computer, which are usually notrecognized by the naked eye (in the case of images), but arerecognizable algorithmically when the signal values are analyzed. Forexample, blood flow may cause facial skin color changes (FSCC) thatcorresponds to different concentrations of oxidized hemoglobin due tovarying blood pressure caused by the cardiac pulse. Similar blood flowdependent effects may be viewed with other types of signals (e.g.,slight changes in cutaneous temperatures due to the flow of blood.

When the blood flow on both sides of the head and/or body are monitored,asymmetric changes may be recognized. These changes are typicallydifferent from symmetric changes that can be caused by factors such asphysical activity (which typically effects the blood flow on both sidesin the same way). An asymmetric change to the blood flow can mean thatone side has been affected by an event, such as a stroke, which does notinfluence the other side. In one example, the asymmetric change to bloodflow comprises a change in blood flow velocity on left side of the facethat is 10% greater or 10% lower than a change in blood flow velocity onone right side of the face. In another example, the asymmetric change toblood flow comprises a change in the volume of blood the flows during acertain period in the left side of the face that is 10% greater or 10%lower than the volume of blood that flows during the certain period inthe right side of the face. In yet another example, the asymmetricchange to blood flow comprises a change in the direction of the bloodflow on one side of the face (e.g., as a result of a stroke), which isnot observed at the symmetric location on the other side of the face.

In some embodiments, the computer detects whether the user has suffereda stroke based on a comparison between values belonging to a current setof S_(BF1) and S_(BF2) and values belonging to a previous set of S_(BF1)and S_(BF2) of the user. Optionally, the previous set was measured atleast fifteen minutes before the current set, at least one hour beforethe current set, or at least one day before the current set. Optionally,the previous set serves to establish baseline blood flow values for bothregions when the user was assumed not to be effected by a stroke.

In one embodiment, the computer uses a machine learning model to detectwhether the user has suffered a stroke. Optionally, the computer isconfigured to: generate feature values based on data comprising acurrent set of S_(BF1) and S_(BF2) and a previous set of S_(BF1) andS_(BF2) of the user, and utilize the model to calculate, based on thefeature values, a value indicative of whether the user has suffered astroke. Optionally, the previous set was measured at least one hourbefore the current set. Optionally, at least some of the feature valuesare indicative of changes to the blood flow in each of the first andsecond regions between the time the current set was measured and whenthe previous set was measured.

In one embodiment, the model was generated based on data comprising“after” sets of S_(BF1) and S_(BF2) of other users and corresponding“before” sets of S_(BF1) and S_(BF2) of the other users. In theseembodiments, the “after” sets were measured after the other users hadsuffered from a stroke (and the “before” sets were measured before theysuffered from the stroke). Thus, training samples generated based onthis data can reflect some of the types of asymmetric changes to bloodflow that characterize suffering from a stroke.

In the event that a stroke is detected, the computer may prompt the userto take a FAST test (or portions thereof), as described above. Inanother example, the computer may suggest to the user to take images ofthe retinas. In this example, the computer is further configured tocompare the images of the retinas with previously taken images of theretinas of the user, and to detect whether the user has suffered astroke based on the comparison. Optionally, the comparison can take intoaccount diameter of retinal arteries, swelling and blurring of theboundaries of the optic disk.

Blood flow usually exhibits typical patterns in the user's body. When ablood flow pattern changes, this usually happens in a predictable way(e.g., increase in blood flow due to physical activity, certainemotional responses, etc.). When a person's blood flow changes in anatypical way, this may indicate an occurrence of a certain medicalincident, such as a stroke. Therefore, atypical blood flow patternsshould be detected in order to investigate their cause.

In some embodiments, a system configured to detect an atypical bloodflow pattern in the head of a user includes one or more head-mounteddevices and a computer. The one or more head-mounted devices areconfigured to measure at least three signals (S_(BF1), S_(BF2) andS_(BF3), respectively), indicative of blood flow in at least threecorresponding regions of interest on the head (ROI₁, ROI₂, and ROI₃,respectively) of a user. Optionally, the centers of ROI₁, ROI₂ and ROI₃are at least 1 cm away from each other. Optionally, the one or morehead-mounted devices do not occlude ROI₁, ROI₂ and ROI₃.

There are various types of devices the one or more head-mounted devicesmay be. Optionally, each of the one or more head-mounted devices are thesame type of device. Alternatively, when the one or more head-mounteddevices are a plurality of devices, the plurality of devices may be ofdifferent types of devices. The following are some examples of varioustypes of devices that can be used in embodiments described herein inorder to measure a signal indicative of blood flow in a region of thebody of the user.

In one embodiment, the one or more head-mounted devices include a camerathat is based on a sensor comprising at least 3×3 pixels configured todetect electromagnetic radiation having wavelengths in at least aportion of the range of 200 nm to 1200 nm. Optionally, the systemincludes one or more light sources configured to ROI₁, ROI₂ and ROI₃.Optionally, the one or more light sources are configured to illuminateROI₁, ROI₂ and ROI₃ with electromagnetic radiation having wavelengths inat least a portion of the range of 800 nm to 1200 nm.

In another embodiment, the one or more head-mounted devices include adevice that functions as an imaging photoplethysmography device and/or adevice that functions as a pulse oximeter.

The computer is configured, in one embodiment, to generate a blood flowpattern based on a current set of S_(BF1), S_(BF2) and S_(BF3). Thecomputer is also configured to calculate, based on a set of previousblood flow patterns of the user, a value indicative of the extent towhich the blood flow pattern is atypical. Optionally, the set ofprevious blood flow patterns were generated based on sets of S_(BF1),S_(BF2) and S_(BF3) measured at least one day prior to when the currentset was measured. That is, the previous sets of blood flow patterns weregenerated based on data the comprises S_(BF1), S_(BF2) and S_(BF3)measured at least one day before S_(BF1), S_(BF2) and S_(BF3) in thecurrent set. Optionally, the previous sets of blood flow patterns weregenerated based on data that was measured when the user was not known tobe in an atypical condition (e.g., the user was considered healthy atthe time).

In some embodiments, the value indicative of the extent to which theblood flow pattern is atypical is compared to a threshold. If the valuereaches the threshold, the computer may take different actions. In oneembodiment, responsive to the value reaching the threshold, the computerissues an alert. For example, the computer may send a message to apredetermined recipient (e.g., emergency services or a caregiver). Inanother example, the computer may command a user interface to generatean alert (e.g., a beeping sound, a text message, or vibrations. Inanother embodiment, responsive to the value reaching the threshold, thecomputer may prompt the user to perform a test, using an electronicdevice, to determine whether the user has suffered a stroke. Forexample, the user may be prompted to perform a FAST test (or portionsthereof), as described elsewhere in this disclosure.

There are various ways in which a blood flow pattern may be generatedbased on S_(BF1), S_(BF2) and S_(BF3). In one embodiment, S_(BF1),S_(BF2) and S_(BF3) themselves may constitute the pattern. Optionally,the blood flow pattern comprises a time series that is indicative ofblood flow at each of ROI₁, ROI₂, and ROI₃. In one example, the bloodflow pattern may be a time series of the raw (unprocessed) values ofS_(BF1), S_(BF2) and S_(BF3) (e.g., a time series of values measured bythe one or more head-mounted devices). In another example, blood flowpattern may be a time series of processed values, such as variousstatistics of S_(BF1), S_(BF2) and S_(BF3) at different points of time(e.g., average pixel intensity for images taken by a camera at differentpoints of time).

In another embodiment, the blood flow pattern may include statistics ofthe values of S_(BF1), S_(BF2) and S_(BF3), such as values representingthe average blood flow during the period S_(BF1), S_(BF2) and S_(BF3)were measured. Values in a blood flow pattern, such as the statistics ofthe values of S_(BF1), S_(BF2) and S_(BF3) may be of various types ofvalues. The following are some examples of types of values that may beused in a “blood flow pattern”. In one example, the blood flow patternincludes one or more values that describe the intensity of blood flow ateach of ROI₁, ROI₂, and/or ROI₃. In another example, the blood flowpattern includes one or more values that describe the amplitude ofchanges to measurement values at ROI₁, ROI₂, and/or ROI₃, such as theamplitude of periodic changes (which correspond to the heart rate) tocolor, temperature, and/or absorbance at certain wavelengths. In yetanother example, the blood flow pattern includes one or more values thatdescribe the speed at which a pulse wave propagates through ROI₁, ROI₂,and/or ROI₃. In still another example, the blood flow pattern includesone or more values that describe the direction at which a pulse wavepropagates in ROI₁, ROI₂, and/or ROI₃.

There are various ways in which the computer may utilize the previousblood flow patterns in order to determine the extent to which the bloodflow pattern is atypical.

In one embodiment, the computer calculates the value indicative of theextent to which the blood flow pattern is atypical by calculatingdistances between the blood flow pattern and each of the previous bloodflow patterns, and determining a minimal value among the distances. Thelarger this minimal value, the more atypical the blood flow pattern maybe considered (due to its difference from all previous blood flowpatterns considered). In one example, in which blood flow patternsinclude time series data, the distances may be calculated by finding theextent of similarity between the blood flow pattern and each of theprevious blood flow patterns (e.g., using methods described in in Wang,Xiaoyue, et al. “Experimental comparison of representation methods anddistance measures for time series data”, Data Mining and KnowledgeDiscovery 26.2 (2013): 275-309). In another example, distances betweenblood flow patterns that include dimensional data, such as blood flowpatterns that may be represented as vectors of values, may be calculatedusing various similarity metrics known in the art such as Euclideandistances or vector dot products.

In another embodiment, the computer calculates, based on the previoussets of S_(BF1), S_(BF2) and S_(BF3), parameters of a probabilitydensity function (pdf) for blood flow patterns. For example, each bloodflow pattern may be represented as a vector of values and the pdf may bea multivariate distribution (e.g., a multivariate Gaussian) whoseparameters are calculated based on vectors of values representing theprevious blood flow patterns. Given a vector of values representing theblood flow pattern, the probability of the vector of values iscalculated based on the parameters of the pdf. Optionally, if theprobability is below a threshold, the blood flow pattern may beconsidered atypical. Optionally, the threshold is determined based onprobabilities calculated for the vectors of values representing theprevious blood flow patterns, such the at least a certain proportion ofthe vectors have a probability that reaches the threshold. For example,the certain proportion may be at least 75%, at least 90%, at least 99%,or 100%.

In yet another embodiment, the computer calculates the value indicativeof the extent to which the blood flow pattern is atypical using a modelof a one-class classifier generated based on the set of previous bloodflow patterns.

In still another embodiment, the computer calculates feature values andutilizes a model to calculate, based on the feature values, the valueindicative of the extent to which the blood flow pattern is atypical.Optionally, one or more of the feature values are generated based on theblood flow pattern, and at least one of the feature values is generatedbased on the previous blood flow patterns. Optionally, feature valuesgenerated based on a blood flow pattern include one or more of thevalues described in examples given above as examples of values in ablood flow pattern. Optionally, one or more of the feature valuesdescribe differences between values representing the blood flow pattern,and values representing the previous blood flow patterns. The model isgenerated based on samples, with each sample comprising: (i) featurevalues calculated based on a blood flow pattern of a certain user, fromamong a plurality of users, and previous blood flow patterns of thecertain user, and (ii) a label indicative of an extent to which theblood flow pattern of the certain user is atypical. Additional detailsregarding generating the model can be found herein in the discussionregarding machine learning approaches that may be used to detect aphysiological response.

In some embodiments, the computer may use additional inputs to determinewhether the blood flow pattern is an atypical blood flow pattern. In oneexample, the computer may receive measurements of various physiologicalsignals (e.g., heart rate, respiration, or brain activity) and use thesemeasurements to generate at least some of the feature values. In anotherexample, the computer may receive an indication of a state of the userand to generate one or more of the feature values based on theindication. Optionally, the state of the user is indicative of at leastone of the following: an extent of physical activity of the user, andconsuming a certain substance by the user (e.g., alcohol or a drug).

The physiological and emotional state of a person can often beassociated with certain cortical activity. Various phenomena, which maybe considered abnormal states, such as anger or displaying symptomaticbehavior of Attention Deficit Disorder (ADD) or Attention DeficitHyperactivity Disorder (ADHD), are often associated with certainatypical cortical activity. This atypical cortical activity can changethe blood flow patterns on the face, and especially on the foreheadarea. Thus, there is a need for a way to detect such changes in bloodflow in real world day-to-day situations. Preferably, in order to becomfortable and more aesthetically acceptable, these measurements shouldbe taken without involving direct physical contact with the forehead oroccluding it.

In some embodiments, a system configured to detect an attackcharacterized by an atypical blood flow pattern includes the one or morehead-mounted devices (described above) and a computer. The one or morehead-mounted devices are configured to measure at least three signals(S_(BF1), S_(BF2) and S_(BF3), respectively), indicative of blood flowin at least three corresponding regions of interest on the head (ROI₁,ROI₂, and ROI₃, respectively) of a user. Optionally, the centers ofROI₁, ROI₂ and ROI₃ are at least 1 cm away from each other. Optionally,the one or more head-mounted devices do not occlude ROI₁, ROI₂ and ROI₃.Optionally, ROI₁, ROI₂ and ROI₃ are on the same side of the face.Alternatively, ROI₁, ROI₂ and ROI₃ are on all on the same side of theface. For example, ROI₁ may be on the left side of the face, and ROI₂ onthe right side of the face.

The computer is configured, in one embodiment, to calculate a value,based on S_(BF1), S_(BF2), and S_(BF3), indicative of whether the useris in a normal or abnormal state.

In one embodiment, the state of the user is determined by comparing ablood flow pattern generated based on S_(BF1), S_(BF2), and S_(BF3) toreference blood flow patterns of the user that include at least onereference blood flow pattern that corresponds to the normal state and atleast one reference blood flow pattern that corresponds to the abnormalstate. Optionally, a reference blood flow pattern is determined fromprevious S_(BF1), S_(BF2), and S_(BF3) of the user, taken while the userwas in a certain state corresponding to the reference blood flow pattern(e.g., normal or abnormal states). Optionally, if the similarity reachesa threshold, the user is considered to be in the state to which thereference blood flow pattern corresponds.

In another embodiment, the computer determines that the user is in acertain state (e.g., normal or abnormal) by generating feature values(at least some of which are generated based on S_(BF1), S_(BF2), andS_(BF3)) and utilizing a model to calculate, based on the featurevalues, the value indicative of whether the user is in a normal orabnormal state. Optionally, the model is trained based on samples, eachcomprising feature values generated based on previous S_(BF1), S_(BF2),and S_(BF3) of the user, taken while the user was in the certain state.

In yet another embodiment, S_(BF1), S_(BF2), and S_(BF3) comprise timeseries data, and the computer calculates the value indicative of whetherthe user is in a normal or abnormal state based on comparing the timeseries to at least first and second reference time series, eachgenerated based on previously taken S_(BF1), S_(BF2), and S_(BF3) of theuser; the first reference time series is based on previous S_(BF1),S_(BF2), and S_(BF3) taken while the user was in a normal state, and thesecond reference time series is based on previous S_(BF1), S_(BF2), andS_(BF3) taken while the user was in an abnormal state. Optionally, thetime series data and/or the first and second reference time seriesincludes data taken over at least a certain period of time (e.g., atleast ten seconds, at least one minute, or at least ten minutes).

Being in a normal/abnormal state may correspond to different behavioraland/or physiological responses. In one embodiment, the abnormal stateinvolves the user displaying symptoms of one or more of the following:an anger attack, Attention Deficit Disorder (ADD), and Attention DeficitHyperactivity Disorder (ADHD). In this embodiment, being in the normalstate refers to usual behavior of the user that does not involvedisplaying said symptoms. In another embodiment, when the user is in theabnormal state, the user will display within a predetermined duration(e.g., shorter than an hour), with a probability above a predeterminedthreshold, symptoms of one or more of the following: anger, ADD, andADHD. In this embodiment, when the user is in the normal state, the userwill display the symptoms within the predetermined duration with aprobability below the predetermined threshold. In yet anotherembodiment, when the user is in the abnormal state the user suffers froma headache and/or migraine (or an onset of a migraine attack will occuris a short time such as less than one hour), and when the user is in thenormal state, the user does not suffer from a headache and/or amigraine. In still another embodiment, the abnormal state refers totimes in which the user has a higher level of concentration compared tothe normal state that refers to time in which the user has a usual levelof concentration. Although the blood flow patterns of the forehead areusually specific to the user, they are usually repetitive, and thus thesystem may able to learn some blood flow patterns of the user thatcorrespond to various states.

Determining the user's state based on S_(BF1), S_(BF2), and S_(BF3) (andoptionally other sources of data) may be done using a machinelearning-based model. Optionally, the model is trained based on samplescomprising feature values generated based on previous S_(BF1), S_(BF2),and S_(BF3) taken when the user was in a known state (e.g., fordifferent times it was known whether the user was in the normal orabnormal state). Optionally, the user may provide indications abouthis/her state at the time, such as by entering values via an app whenhaving a headache, migraine, or an anger attack. Additionally oralternatively, an observer of the user, which may be another person or asoftware agent, may provide the indications about the user's state. Forexample, a parent may determine that certain behavior patterns of achild correspond to displaying symptomatic behavior of ADHD. In anotherexample, indications of the state of the user may be determined based onmeasurements of physiological signals of the user, such as measurementsof the heart rate, heart rate variability, breathing rate, galvanic skinresponse, and/or brain activity (e.g., using EEG). Optionally, thevarious indications described above are used to generate labels for thesamples generated based on the previous S_(BF1), S_(BF2), and S_(BF3).

In some embodiments, one or more of the feature values in the samplesmay be based on other sources of data (different from S_(BF1), S_(BF2),and S_(BF3)). These may include additional physiological measurements ofthe user and/or measurements of the environment in which the user waswhile S_(BF1), S_(BF2), and S_(BF3) were taken. In one example, at leastsome of the feature values used in samples include additionalphysiological measurements indicative of one or more of the followingsignals of the user: heart rate, heart rate variability, brainwaveactivity, galvanic skin response, muscle activity, and extent ofmovement. In another example, at least some of the feature values usedin samples include measurements of the environment that are indicativeof one or more of the following values of the environment in which theuser was in: temperature, humidity level, noise level, air quality, windspeed, and infrared radiation level.

Given a set of samples comprising feature values generated based onS_(BF1), S_(BF2), and S_(BF3) (and optionally the other sources of data)and labels generated based on the indications, the model can be trainedusing various machine learning-based training algorithms. Optionally,the model is utilized by a classifier that classifies the user's state(e.g., normal/abnormal) based on feature values generated based onS_(BF1), S_(BF2), and S_(BF3) (and optionally the other sources).

The model may include various types of parameters, depending on the typeof training algorithm utilized to generate the model. For example, themodel may include parameters of one or more of the following: aregression model, a support vector machine, a neural network, agraphical model, a decision tree, a random forest, and other models ofother types of machine learning classification and/or predictionapproaches.

In some embodiments, the model is trained utilizing deep learningalgorithms. Optionally, the model includes parameters describingmultiple hidden layers of a neural network. Optionally, the modelincludes a convolution neural network (CNN), which is useful foridentifying certain patterns in images, such as patterns of colorchanges on the forehead. Optionally, the model may be utilized toidentify a progression of a state of the user (e.g., a gradual formingof a certain blood flow pattern on the forehead). In such cases, themodel may include parameters that describe an architecture that supportsa capability of retaining state information. In one example, the modelmay include parameters of a recurrent neural network (RNN), which is aconnectionist model that captures the dynamics of sequences of samplesvia cycles in the network's nodes. This enables RNNs to retain a statethat can represent information from an arbitrarily long context window.In one example, the RNN may be implemented using a long short-termmemory (LSTM) architecture. In another example, the RNN may beimplemented using a bidirectional recurrent neural network architecture(BRNN).

In order to generate a model suitable for identifying the state of theuser in real-world day-to-day situations, in some embodiments, thesamples used to train the model are based on S_(BF1), S_(BF2), andS_(BF3) (and optionally the other sources of data) taken while the userwas in different situations, locations, and/or conducting differentactivities. In a first example, the model may be trained based on afirst set of previous S_(BF1), S_(BF2), and S_(BF3) taken while the userwas indoors and in the normal state, a second set of previous S_(BF1),S_(BF2), and S_(BF3) taken while the user was indoors and in theabnormal state, a third set of previous S_(BF1), S_(BF2), and S_(BF3)taken while the user was outdoors and in the normal state, and a fourthset of previous S_(BF1), S_(BF2), and S_(BF3) taken while the user wasoutdoors and in the abnormal state. In a second example, the model maybe trained based on a first set of previous S_(BF1), S_(BF2), andS_(BF3) taken while the user was sitting and in the normal state, asecond set of previous S_(BF1), S_(BF2), and S_(BF3) taken while theuser was sitting and in the abnormal state, a third set of previousS_(BF1), S_(BF2), and S_(BF3) taken while the user was standing and/ormoving around and in the normal state, and a fourth set of previousS_(BF1), S_(BF2), and S_(BF3) taken while the user was standing and/ormoving around and in the abnormal state. Usually the movements whilestanding and/or moving around, and especially when walking or running,are greater compared to the movement while sitting; therefore, a modeltrained on samples taken during both sitting and standing and/or movingaround is expected to perform better compared to a model trained onsamples taken only while sitting.

Having the ability to determine the state of the user can beadvantageous when it comes to scheduling tasks for the user and/ormaking recommendations for the user, which suits the user's state. Inone embodiment, responsive to determining that the user is in the normalstate, the computer prioritizes a first activity over a second activity,and responsive to determining that the user is in the abnormal state,the computer prioritizes the second activity over the first activity.Optionally, accomplishing each of the first and second activitiesrequires at least a minute of the user's attention, and the secondactivity is more suitable for the abnormal state than the firstactivity. Optionally, and the first activity is more suitable for thenormal state than the second activity. Optionally, prioritizing thefirst and second activities is performed by a calendar managementprogram, a project management program, and/or a “to do” list program.Optionally, prioritizing a certain activity over another means one ormore of the following: suggesting the certain activity before suggestingthe other activity, suggesting the certain activity more frequently thanthe other activity (in the context of the specific state), allottingmore time for the certain activity than for the other activity, andgiving a more prominent reminder for the certain activity than for theother activity (e.g., an auditory indication vs. a mention in a calendarprogram that is visible only if the calendar program is opened).

Such state-dependent prioritization may be implemented in variousscenarios. In one example, the normal state refers to a normalconcentration level, the abnormal state refers to a lower than normalconcentration level, and the first activity requires a high attentionlevel from the user compared to the second activity. For instance, thefirst and second activities may relate to different topics of aself-learning program for school; when identifying that the user is inthe normal concentration state, a math class is prioritized higher thana sports lesson; and when identifying that the user is in the lowerconcentration state, the math class is prioritized lower than the sportslesson. In another example, the normal state refers to a normal angerlevel, the abnormal state refers to a higher than normal anger level,and the first activity involves more interactions of the user with otherhumans compared to the second activity. In still another example, thenormal state refers to a normal fear level, the abnormal state refers toa panic attack, and the second activity is expected to have a morerelaxing effect on the user compared to the first activity.

Passively taken sensor based measurements may be used, in someembodiments, in order to detect whether a user exhibits stroke signs.However, in some scenarios, sensors that may be used to detect strokesigns may provide inaccurate signals that can lead to a high rate offalse alarms. Detecting whether a user exhibits stroke signs can also bedone by an app that prompts the user to perform one or more activitiesinvolved in a FAST test, and analyzing the user's actions whileperforming the one or more activities. However, apps for detectingstroke signs are often not used because the person suffering from astroke is not aware of the incident (and thus does not initiate a test).Thus, the combination of the two approaches can increase the rate atwhich stroke signs may be detected based on possibly inaccuratemeasurements, by having the sensor measurements serve as a trigger toactivate an app to prompt the user to perform the one or more activitiesinvolved in the FAST test.

In one embodiment, a system configured detect stroke signs includes atleast first, second, and third sensors, and a computer.

The first and second sensors configured to take first and secondmeasurements (M_(R) and M_(L), respectively) of regions belonging to theright and left sides of a user's body. Optionally, M_(R) and M_(L) areindicative of blood flow in the regions on the right and left sides ofthe user's body, respectively. The following are examples of varioustypes of sensors that may be used, in some embodiments, as the first andsecond sensors.

In one example, the first and second sensors are electroencephalography(EEG) electrodes that measure brain activity and are positioned on theright and left sides of the head, respectively. Optionally, the firstand second sensors are EEG electrodes implanted under the user's scalp.

In another example, the first and second sensors are electromyography(EMG) sensors implanted in the left and right sides of the user's body,respectively. Alternatively, the first and second sensors may be EMGsensors attached to the surface of the user's body.

In yet another example, the first and second sensors may be ultrasoundsensors. Optionally, the ultrasound sensors are positioned such thatthey are in contact with the surface of the right and left sides of theuser's body respectively.

In still another example, the first and second sensors arephotoplethysmogram (PPG) sensors that provide measurements indicative ofthe blood flow on the right and left sides of the user's body,respectively.

In yet another example, the first and second sensors comprise camerasbased on a sensor comprising at least 3×3 pixels configured to detectelectromagnetic radiation having wavelengths in at least a portion ofthe range of 200 nm to 1200 nm.

In yet another example, the first and second sensors are thermistors incontact with the right and left sides of the user's body. For example,the first and second sensors may be embedded in one or more of thefollowing: a clothing item worn by the user, gloves, or a scarf.

And in still another example, the first and second sensors are thermalcameras each comprising at least 3×3 pixels configured to detectelectromagnetic radiation having wavelengths above 2500 nm.

The computer calculates, based on M_(R) and M_(L), a value indicative ofa risk that the user has suffered from a stroke. Responsive todetermining that the value reaches a threshold, the computer mayinstruct the user, via a user interface, to perform the predeterminedactivity. The computer may then detect whether the user exhibits strokesigns based on measurements (M_(A)) of a third sensor, which were takenwhile the user performed the predetermined activity. Optionally, thecomputer utilizes M_(R) and M_(L) along with M_(A) in order to make amore accurate detection of stroke signs. Optionally, detection based onM_(R), M_(L), and M_(A) is more accurate than detection based on M_(R)alone M_(L).

Calculating the value indicative of the risk that the user has sufferedfrom a stroke may be done in different ways in different embodiments. Inone embodiment, the value indicative of the risk is indicativedifference between of extents of physiological changes in the right andleft sides of the body, which are calculated based on M_(R) and M_(L),respectively. For example, the value may be indicative of a differencein blood flow between the right and left sides of the body, a differencein the temperature between the right and left sides, and/or a differencein skin color and/or extent of changes to skin color between the rightand left sides. Optionally, when the difference reaches the threshold,this means that the user is at a risk of having suffered a stroke thatis sufficiently high to warrant an additional test that involvesperforming the predetermined activity and measuring the user with thethird sensor.

In another embodiment, the computer generates feature values based onM_(R) and M_(L) and utilizes a model to calculate, based on the featurevalues, the value indicative of the risk that the user has suffered froma stroke. Optionally, the model is generated based on previous M_(R) andM_(L) of multiple users. For example, the model may be generated basedon data that comprises M_(R) and M_(L) of users who were healthy at thetime the measurements were taken, and also based on M_(R) and M_(L) ofusers who suffered from a stroke while the measurements were taken.

There are various predetermined activities the computer may prompt theuser to perform in order to detect whether the user exhibits strokesigns. These activities may be measured using different sensors. Thefollowing are examples of combinations of activities, and sensors thatmay be used to take the measurements M_(A) of the user while the userperforms the activities.

In one embodiment, performing the predetermined activity comprisessmiling. Optionally, in this embodiment, the third sensor is a camera.For example, the camera may be a camera embedded in a cell phone (orsome other device) held by the user or some other person. In anotherexample, the camera may be coupled to a frame worn on the user's head.In this embodiment, M_(A) include images of the user's face that capturethe user's facial expressions while the user was instructed to smile.The computer may detect the stroke signs based on analysis of images ofthe user which are indicative of one side of the face drooping.Optionally, the analysis involves comparing M_(A) to previously takenimages of the user's face.

In another embodiment, performing the predetermined activity comprisesraising both arms. Optionally, in this embodiment, the third sensor isan inertial measurement unit (IMU). For example, the IMU may be embeddedin a cell phone, a smart watch, or another device on worn or held by theuser (e.g., on the hand, wrist, or arm). In this embodiment, thecomputer detects the stroke signs based on analysis of measurements ofmovements of the user (taken by the IMU) which are indicative of one armdrifting downward.

In yet another embodiment, performing the predetermined activitycomprises walking. Optionally, in this embodiment, the third sensor isan inertial measurement unit (IMU). For example, the IMU may be coupledto a frame worn on the user's head or in a device carried by the user(e.g., a smartphone or a smartwatch). In this embodiment, computerdetects the stroke signs based on analysis measurements of movements ofthe user (taken by the IMU) which are indicative of imbalance of theuser.

In still another embodiment, performing the predetermined activitycomprises repeating a phrase. Optionally, in this embodiment, the thirdsensor is a microphone. For example, the microphone may belong to adevice carried or worn by the user (e.g., a smartphone, a smartwatch, ormicrophone embedded in a head-mounted display). In this embodiment,computer detects the stroke signs based on analysis of a recording ofvoice of the user which is indicative of the user's speech being slurry.Optionally, the analysis of the recording of the user's voice is donebased on a comparison with previous recordings of the user's voice.

In some embodiments, the computer may conduct tests to determine whetherthe user's brain function has been affected. For example, the computermay prompt the user to perform various tasks using an app that can testfor memory and cognitive skills, such as answering trivia questions,solving puzzles, etc.

In some embodiments, following the user's performance of thepredetermined activity and analysis of M_(A), results of the analysismay be used to improve the accuracy of the calculation of the risk. Forexample, responsive to determining, based on specific M_(A), that theuser does not exhibit stroke signs after measuring specific M_(R) andM_(L) values (which indicated that there is sufficient risk), thecomputer is configured to adjust the threshold based on the specificM_(R) and M_(L) values. In another example, the computer may utilize thespecific M_(R) and M_(L) to retrain a model used to calculate the risk.In this example, the specific M_(R) and M_(L) can constitute an exampleof a false positive that is used to adjust the model parameters.

One of the signs to having a stroke is sudden loss of balance orcoordination. It is easier to identify loss of balance or coordinationfrom measurements taken by a head-mounted IMU compared to measurementstaken by an IMU inside a wrist band or a phone in the hand. The reasonis that the movements of the head include much less noise compared tomovements of a hand that is used to manipulate items. Therefore, ahead-mounted IMU can be more beneficial for identifying loss of balanceor coordination than a wrist-mounted IMU.

In one embodiment, a system configured to detect a medical condition,which involves a loss of at least one of balance and coordination,includes at least a head-mounted inertial measurement unit (IMU) and acomputer. Some examples of medical conditions that involve loss ofbalance and/or coordination include a stroke and a spell of dizziness.

The IMU is configured to measure movements of the head of a user wearingthe IMU, and a computer. Optionally, the IMU comprises at least one ofthe following elements: an accelerometer, a gyroscope, and amagnetometer. Optionally, the IMU is coupled to a frame worn on theuser's head.

The computer is configured to detect the medical condition based onmeasurements of the IMU (M_(current)) and previous measurements ofmovements of the head of the user wearing the IMU (M_(previous)).Optionally, at least some of M_(previous) were taken while the user didnot have the medical condition. Optionally, the computer is furtherconfigured to alert a predetermined recipient (e.g., a caregiver oremergency services) responsive to detecting the medical condition.

There are various ways in which the medical condition may be detectedbased on M_(current) and M_(previous). In one embodiment, the computeris configured to detect the medical condition by calculating adifference between movement characteristics determined based onM_(current) and typical movement characteristics of the user calculatedbased on M_(previous). Optionally, if the difference reaches athreshold, the user is considered to have the medical condition.

In another embodiment, the computer is configured to detect the medicalcondition by generating feature values and utilizing a model tocalculate a value, based on the feature values, which is indicative ofwhether the user has the medical condition. Optionally, one or more ofthe feature values are calculated based on M_(current) and the model isgenerated based M_(previous) of the user.

In yet another embodiment, the computer is configured to detect themedical condition by generating feature values and utilizing a model tocalculate a value, based on the feature values, which is indicative ofwhether the user has the medical condition. Optionally, one or more ofthe feature values are calculated based on movements of the head of oneor more other users wearing an IMU.

The user's movement characteristics may be affected by various factors,as discussed below. Thus, receiving an indication of such factors thatinfluence movement and balance can help improve accuracy of detectingthe medical condition based on M_(current) and M_(previous). In someembodiments, the computer is further configured to receive an indicationof a situation in which the user is in while M_(current) are taken, andto utilize the indication to detect the medical condition.

In one embodiment, the situation comprises being under influence of amedication, and the indication of the situation is received from atleast one of: a pill dispenser, a sensor-enabled pill, and a usertracking application. Examples of user tracking applications include:(i) software that requires a user to log events, such as consumption ofa certain item, usage of a certain item, having a certain experience,and/or being in a certain situation, (ii) an image analysis softwarethat receives images taken by the user or a third party nearby the user,and infers the situation from the images using image processingtechniques. Examples of sources for the image include: smart-glasseswith outfacing camera, augmented reality devices, mobile phones, andwebcams.

In another one embodiment, the situation comprises being under influenceof at least one of alcohol and caffeine, and the indication of thesituation is received from at least one of: a refrigerator, a pantry, aserving robot, and a user tracking application; and wherein theindication indicates that the user took an alcoholic beverage.

In yet another embodiment, the situation comprises being under a certainstress level, and the indication of the situation is received from asensor that measures a physiological signal of the user, such as athermal sensor, a heart rate sensor, a sensor of galvanic skin response,or an EEG sensor.

In still another embodiment, the situation is selected from a groupcomprising: being inside a moving vehicle, and being on a static floor;and the indication of the situation is received from a positioningsystem. Examples of positioning systems include: GPS, wirelesspositioning systems, image processing to infer position.

Typically having a stroke will influence brain activity, which can bedetected through electroencephalography (EEG). However, this signal mayat times be noisy and insufficient to reliably detect a stroke. Thus,additional tests may be needed.

In one embodiment, a system configured to detect stroke signs based onelectroencephalography (EEG) includes at least a sensor configured totake measurements (M_(A)) of the user, and a computer.

The computer is configured to: receive EEG signals of the user, and toutilize a model to calculate, based on the EEG signals, a valueindicative of a risk that the user has suffered from a stroke. The valueindicative of the risk is compared by the computer to a threshold, andresponsive to determining that the value reaches a threshold, thecomputer instructs the user, via a user interface, to perform apredetermined activity. The computer then detects whether the user hasstroke signs based on M_(A) taken while the user performed thepredetermined activity. Optionally, the computer utilizes the EEGsignals along with M_(A) in order to make a more accurate detection ofstroke signs. Optionally, detection based on the EEG signals and M_(A)is more accurate than detection based on the EEG signals alone.

Calculating the value indicative of the risk that the user has sufferedfrom a stroke may be done in different ways in different embodiments. Inone embodiment, the value indicative of the risk is indicativedifference between of extents of electrical potential changes in theright and left sides of the brain, which are calculated based on EEGsignals from electrodes on the right and left sides of the head.Optionally, when the difference reaches the threshold, this means thatthe user is at a risk of having suffered a stroke that is sufficientlyhigh to warrant an additional test that involves performing thepredetermined activity and measuring the user with the third sensor.

In another embodiment, the computer generates feature values based onthe EEG signals and utilizes a model to calculate, based on the featurevalues, the value indicative of the risk that the user has suffered froma stroke. Optionally, the model is generated based on previous EEGsignals of multiple users. For example, the model may be generated basedon data that comprises EEG signals of users who were healthy at the timethe measurements were taken, and also based on EEG signals of users whosuffered from a stroke while the measurements were taken.

There are various predetermined activities the computer may prompt theuser to perform in order to detect whether the user exhibits strokesigns. These activities may be measured using different sensors.Examples of combinations of activities, and sensors that may be used totake the measurements M_(A) of the user while the user performs theactivities are given above (e.g., smiling, raising hands, walking, andspeaking a phrase).

In some embodiments, following the user's performance of thepredetermined activity and analysis of M_(A) results of the analysis maybe used to improve the accuracy of the calculation of the risk. Forexample, responsive to determining, based on specific M_(A), that theuser does not exhibit stroke signs after measuring specific EEG signalsvalues (which indicated that there is sufficient risk), the computer isconfigured to adjust the threshold based on the specific EEG signals. Inanother example, the computer may utilize the specific EEG signals toretrain a model used to calculate the risk. In this example, thespecific EEG signals can constitute an example of a false positive thatis used to adjust the model parameters.

Normally, the lens plane and the sensor plane of a camera are parallel,and the plane of focus (PoF) is parallel to the lens and sensor planes.If a planar object is also parallel to the sensor plane, it can coincidewith the PoF, and the entire object can be captured sharply. If the lensplane is tilted (not parallel) relative to the sensor plane, it will bein focus along a line where it intersects the PoF. The Scheimpflugprinciple is a known geometric rule that describes the orientation ofthe plane of focus of a camera when the lens plane is tilted relative tothe sensor plane.

FIG. 35a is a schematic illustration of an inward-facing head-mountedcamera 550 embedded in an eyeglasses frame 551, which utilizes theScheimpflug principle to improve the sharpness of the image taken by thecamera 550. The camera 550 includes a sensor 558 and a lens 555. Thetilt of the lens 555 relative to sensor 558, which may also beconsidered as the angle between the lens plane 555 and the sensor plane559, is determined according to the expected position of the camera 550relative to the ROI 552 when the user wears the eyeglasses. For arefractive optical lens, the “lens plane” 556 refers to a plane that isperpendicular to the optical axis of the lens 555. Herein, the singularalso includes the plural, and the term “lens” refers to one or morelenses. When “lens” refers to multiple lenses (which is usually the casein most modern cameras having a lens module with multiple lenses), thenthe “lens plane” refers to a plane that is perpendicular to the opticalaxis of the lens module.

The Scheimpflug principle may be used for both thermal cameras (based onlenses and sensors for wavelengths longer than 2500 nm) andvisible-light and/or near-IR cameras (based on lenses and sensors forwavelengths between 400-900 nm). FIG. 35b is a schematic illustration ofa camera that is able to change the relative tilt between its lens andsensor planes according to the Scheimpflug principle. Housing 311 mountsa sensor 312 and lens 313. The lens 313 is tilted relative to the sensor312. The tilt may be fixed according to the expected position of thecamera relative to the ROI when the user wears the HMS, or may beadjusted using motor 314. The motor 314 may move the lens 313 and/or thesensor 312.

Because the face is not planar and the inward-facing head-mounted camerais located close to the face, an image captured by a camera having awide field of view (FOV) and a low f-number may not be perfectly sharp,even after applying the Scheimpflug principle. Therefore, in someembodiments, the tilt between the lens plane and the sensor plane isselected such as to adjust the sharpness of the various areas covered inthe ROI according to their importance for detecting the user'sphysiological signals. In one embodiment, the ROI covers first andsecond areas, where the first area includes finer details and/or is moreimportant for detecting the physiological signals than the second area.Therefore, the tilt between the lens and sensor planes is adjusted suchthat the image of the first area is shaper than the image of the secondarea.

In one embodiment, the tilt between the lens plane and sensor plane isfixed. The fixed tilt is selected according to an expected orientationbetween the camera and the ROI when a user wears the frame.

In another embodiment, the system includes an adjustableelectromechanical tilting mechanism configured to change the tiltbetween the lens and sensor planes according to the Scheimpflugprinciple based on the orientation between the camera and the ROI whenthe frame is worn by the user. The tilt may be achieved using at leastone motor, such as a brushless DC motor, a stepper motor (without afeedback sensor), a brushed DC electric motor, a piezoelectric motor,and/or a micro-motion motor.

Various embodiments described herein involve an HMS that may beconnected, using wires and/or wirelessly, with a device carried by theuser and/or a non-wearable device. The HMS may include a battery, acomputer, sensors, and a transceiver.

FIG. 36a and FIG. 36b are schematic illustrations of possibleembodiments for computers (400, 410) that are able to realize one ormore of the embodiments discussed herein that include a “computer”. Thecomputer (400, 410) may be implemented in various ways, such as, but notlimited to, a server, a client, a personal computer, a network device, ahandheld device (e.g., a smartphone), an HMS (such as smart glasses, anaugmented reality system, and/or a virtual reality system), a computingdevice embedded in a wearable device (e.g., a smartwatch or a computerembedded in clothing), a computing device implanted in the human body,and/or any other computer form capable of executing a set of computerinstructions. Herein, an augmented reality system refers also to a mixedreality system. Further, references to a computer or processor includeany collection of one or more computers and/or processors (which may beat different locations) that individually or jointly execute one or moresets of computer instructions. For example, a first computer may beembedded in the HMS that communicates with a second computer embedded inthe user's smartphone that communicates over the Internet with a cloudcomputer.

The computer 400 includes one or more of the following components:processor 401, memory 402, computer readable medium 403, user interface404, communication interface 405, and bus 406. The computer 410 includesone or more of the following components: processor 411, memory 412, andcommunication interface 413.

Functionality of various embodiments may be implemented in hardware,software, firmware, or any combination thereof. If implemented at leastin part in software, implementing the functionality may involve acomputer program that includes one or more instructions or code storedor transmitted on a computer-readable medium and executed by one or moreprocessors. Computer-readable media may include computer-readablestorage media, which corresponds to a tangible medium such as datastorage media, or communication media including any medium thatfacilitates transfer of a computer program from one place to another.Computer-readable medium may be any media that can be accessed by one ormore computers to retrieve instructions, code, data, and/or datastructures for implementation of the described embodiments. A computerprogram product may include a computer-readable medium. In one example,the computer-readable medium 403 may include one or more of thefollowing: RAM, ROM, EEPROM, optical storage, magnetic storage, biologicstorage, flash memory, or any other medium that can store computerreadable data.

A computer program (also known as a program, software, softwareapplication, script, program code, or code) can be written in any formof programming language, including compiled or interpreted languages,declarative or procedural languages. The program can be deployed in anyform, including as a standalone program or as a module, component,subroutine, object, or another unit suitable for use in a computingenvironment. A computer program may correspond to a file in a filesystem, may be stored in a portion of a file that holds other programsor data, and/or may be stored in one or more files that may be dedicatedto the program. A computer program may be deployed to be executed on oneor more computers that are located at one or more sites that may beinterconnected by a communication network.

Computer-readable medium may include a single medium and/or multiplemedia (e.g., a centralized or distributed database, and/or associatedcaches and servers) that store one or more sets of instructions. Invarious embodiments, a computer program, and/or portions of a computerprogram, may be stored on a non-transitory computer-readable medium, andmay be updated and/or downloaded via a communication network, such asthe Internet. Optionally, the computer program may be downloaded from acentral repository, such as Apple App Store and/or Google Play.Optionally, the computer program may be downloaded from a repository,such as an open source and/or community run repository (e.g., GitHub).

At least some of the methods described herein are “computer-implementedmethods” that are implemented on a computer, such as the computer (400,410), by executing instructions on the processor (401, 411).Additionally, at least some of these instructions may be stored on anon-transitory computer-readable medium.

As used herein, references to “one embodiment” (and its variations) meanthat the feature being referred to may be included in at least oneembodiment of the invention. Moreover, separate references to “oneembodiment”, “some embodiments”, “another embodiment”, “still anotherembodiment”, etc., may refer to the same embodiment, may illustratedifferent aspects of an embodiment, and/or may refer to differentembodiments.

Some embodiments may be described using the verb “indicating”, theadjective “indicative”, and/or using variations thereof. Herein,sentences in the form of “X is indicative of Y” mean that X includesinformation correlated with Y, up to the case where X equals Y. Statingthat “X indicates Y” or “X indicating Y” may be interpreted as “X beingindicative of Y”. Additionally, sentences in the form of“provide/receive an indication indicating whether X happened” may referherein to any indication method, including but not limited to:sending/receiving a signal when X happened and not sending/receiving asignal when X did not happen, not sending/receiving a signal when Xhappened and sending/receiving a signal when X did not happen, and/orsending/receiving a first signal when X happened and sending/receiving asecond signal X did not happen.

Herein, “most” of something is defined as above 51% of the something(including 100% of the something). Both a “portion” of something and a“region” of something refer herein to a value between a fraction of thesomething and 100% of the something. For example, sentences in the formof a “portion of an area” may cover between 0.1% and 100% of the area.As another example, sentences in the form of a “region on the user'sforehead” may cover between the smallest area captured by a single pixel(such as 0.1% or 5% of the forehead) and 100% of the forehead. The word“region” refers to an open-ended claim language, and a camera said tocapture a specific region on the face may capture just a small part ofthe specific region, the entire specific region, and/or a portion of thespecific region together with additional region(s).

The terms “comprises,” “comprising,” “includes,” “including,” “has,”“having”, or any other variation thereof, indicate an open-ended claimlanguage that does not exclude additional limitations. The “a” or “an”is employed to describe one or more, and the singular also includes theplural unless it is obvious that it is meant otherwise. For example, “acomputer” refers to one or more computers, such as a combination of awearable computer that operates together with a cloud computer.

The phrase “based on” is intended to mean “based, at least in part, on”.Additionally, stating that a value is calculated “based on X” andfollowing that, in a certain embodiment, that the value is calculated“also based on Y”, means that in the certain embodiment, the value iscalculated based on X and Y.

The terms “first”, “second” and so forth are to be interpreted merely asordinal designations, and shall not be limited in themselves. Apredetermined value is a fixed value and/or a value determined any timebefore performing a calculation that compares a certain value with thepredetermined value. A value is also considered to be a predeterminedvalue when the logic, used to determine whether a threshold thatutilizes the value is reached, is known before start performingcomputations to determine whether the threshold is reached.

The embodiments of the invention may include any variety of combinationsand/or integrations of the features of the embodiments described herein.Although some embodiments may depict serial operations, the embodimentsmay perform certain operations in parallel and/or in different ordersfrom those depicted. Moreover, the use of repeated reference numeralsand/or letters in the text and/or drawings is for the purpose ofsimplicity and clarity and does not in itself dictate a relationshipbetween the various embodiments and/or configurations discussed. Theembodiments are not limited in their applications to the order of stepsof the methods, or to details of implementation of the devices, set inthe description, drawings, or examples. Moreover, individual blocksillustrated in the figures may be functional in nature and therefore maynot necessarily correspond to discrete hardware elements.

Certain features of the embodiments, which may have been, for clarity,described in the context of separate embodiments, may also be providedin various combinations in a single embodiment. Conversely, variousfeatures of the embodiments, which may have been, for brevity, describedin the context of a single embodiment, may also be provided separatelyor in any suitable sub-combination. Embodiments described in conjunctionwith specific examples are presented by way of example, and notlimitation. Moreover, it is evident that many alternatives,modifications, and variations will be apparent to those skilled in theart. It is to be understood that other embodiments may be utilized andstructural changes may be made without departing from the scope of theembodiments. Accordingly, this disclosure is intended to embrace allsuch alternatives, modifications, and variations that fall within thespirit and scope of the appended claims and their equivalents.

We claim:
 1. A system configured to detect an abnormal medical event,comprising: at least one right-side head-mounted device configured tomeasure at least two signals indicative of photoplethysmographic signals(PPG_(SR1) and PPG_(SR2), respectively) at first and second regions ofinterest (ROI_(R1) and ROI_(R2), respectively) on the right side of auser's head; wherein ROI_(R1) and ROI_(R2) are located at least 2 cmapart; at least one left-side head-mounted device configured to measureat least two signals indicative of photoplethysmographic signals(PPG_(SL1) and PPG_(SL2), respectively) at first and second regions ofinterest (ROI_(L1) and ROI_(L2), respectively) on the left side of theuser's head; wherein ROI_(L1) and ROI_(L2) are located at least 2 cmapart; and a computer configured to detect the abnormal medical eventbased on an asymmetrical change to blood flow recognizable in PPG_(SR1),PPG_(SR2), PPG_(SL1), and PPG_(SL2).
 2. The system of claim 1, whereinthe asymmetrical change to the blood flow corresponds to a deviation ofPPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2) compared to a baselinebased on previous measurements of PPG_(SR1), PPG_(SR2), PPG_(SL1), andPPG_(SL2) of the user, taken before the abnormal medical event.
 3. Thesystem of claim 2, wherein the computer is further configured togenerate feature values based on data comprising: (i) PPG_(SR1),PPG_(SR2), PPG_(SL1), and PPG_(SL2) of the user, and (ii) the previousmeasurements of PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2) of theuser; and wherein the computer is further configured to utilize a modelto calculate, based on the feature values, a value indicative of whetherthe user is experiencing the abnormal medical event.
 4. The system ofclaim 3, wherein the feature values comprise a certain feature valueindicative of a difference in maximal amplitudes of one or more of thefollowing pairs: (i) PPG_(SR1) and PPG_(SR2), (ii) PPG_(SR1) andPPG_(SL1), and (iii) PPG_(SR1) and PPG_(SL2).
 5. The system of claim 3,wherein the feature values comprise a certain feature value indicativeof a difference in a pulse arrival time between the following pairs ofregions of interest: (i) ROI_(R1) and ROI_(R2), (ii) ROI_(R1) andROI_(L1), and (iii) ROI_(R1) and ROI_(L2).
 6. The system of claim 2,wherein the abnormal medical event is ischemic stroke, and the deviationinvolves an increase in asymmetry between blood flow on the left side ofthe head and blood flow on the right side of the head, with respect tobaseline asymmetry of the user between blood flow on the left side ofthe head and blood flow on the right side of the head.
 7. The system ofclaim 2, wherein the abnormal medical event is ischemic stroke, and thedeviation involves a monotonic increase in a variation between bloodflow at ROI_(R1) and ROI_(R2), with respect to a baseline variationbetween blood flow at ROI_(R1) and ROI_(R2), during a period longer than10 minutes.
 8. The system of claim 7, wherein ROI_(R1) is located inproximity of the mastoid process behind the right ear, and ROI_(R2) islocated before of the right ear.
 9. The system of claim 2, wherein theabnormal medical event is migraine attack, and the deviation isindicative of a pattern of a certain change to facial blood flow, whichis associated with a pattern of a change to facial blood flow of atleast one previous migraine attack, determined based on data comprisingprevious PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2), which weremeasured starting from at least 5 minutes before the previous migraineattack.
 10. The system of claim 2, wherein the abnormal medical event isheadache, and the deviation is indicative of at least one of: a changein directionality of facial blood flow, and an asymmetrical reduction inblood flow to one side of the face.
 11. The system of claim 2, whereinthe at least one right-side head-mounted device comprises first andsecond contact photoplethysmographic devices (PPG₁, PPG₂, respectively),and the at least one left-side head-mounted device comprise third andfourth contact photoplethysmographic devices (PPG₃, PPG₄, respectively);wherein PPG₁, PPG₂, PPG₃, and PPG₄ are physically coupled to aneyeglasses frame, PPG₁ and PPG₃ are in contact with the nose, and PPG₂and PPG₄ are in contact with regions in the vicinities of the ears,respectively.
 12. The system of claim 2, wherein the at least oneright-side head-mounted device comprises first and second contactphotoplethysmographic devices, and the at least one left-sidehead-mounted device comprise third and fourth contactphotoplethysmographic devices.
 13. The system of claim 2, wherein the atleast one right-side head-mounted device comprises a first inward-facingcamera located more than 0.5 cm away from ROI_(R1) and ROI_(R2), andPPG_(SR1) and PPG_(SR2) are recognizable from color changes in regionsin images taken by the first inward-facing camera; and wherein the atleast one left-side head-mounted device comprise a second inward-facingcamera located more than 0.5 cm away from ROI_(L1) and ROI_(L2), andPPG_(SL1) and PPG_(SL2) are recognizable from color changes in regionsin images taken by the second inward-facing camera.
 14. The system ofclaim 13, wherein each of the first and second inward-facing camerasutilizes a sensor having more than 30 pixels, and each of ROI_(R1) andROI_(L1) covers an area greater than 6 cm{circumflex over ( )}2 on theuser's right and left cheeks, respectively, which is illuminated byambient light.
 15. The system of claim 13, wherein each of the first andsecond inward-facing cameras utilizes a sensor having more than 20pixels, and each of ROI_(R1) and ROI_(L1) covers an area greater than 4cm{circumflex over ( )}2 on the right and left sides of the user'sforehead, respectively, which is illuminated by ambient light.
 16. Thesystem of claim 2, further comprising first and second outward-facinghead-mounted cameras configured to take images of the environment to theright and left of the user's head, respectively; and wherein thecomputer is further configured to utilize the images of the environmentto improve the accuracy of detecting the abnormal medical event.
 17. Thesystem of claim 1, further comprising right and left head-mountedthermometers, located at least 2 cm to the right and to the left of avertical symmetry axis that divides the face, respectively, and areconfigured to provide right and left temperature measurements,respectively; and the computer is further configured to detect theabnormal medical event also based on a deviation of the right and lefttemperature measurements from a baseline temperature for the user;wherein the baseline temperature for the user is calculated based ondata comprising previous right and left temperature measurements of theuser, taken more than a day before the abnormal medical event; andwherein the abnormal medical event is selected from a group comprisingcellulitis and dermatitis.
 18. The system of claim 1, further comprisingright and left head-mounted thermometers, located less than 4 cm fromthe right and left earlobes, respectively, and are configured to provideright and left temperature measurements, respectively; and the computeris further configured to detect the abnormal medical event also based ona deviation of the right and left temperature measurements from abaseline temperature for the user; wherein the baseline temperature forthe user is calculated based on data comprising previous right and lefttemperature measurements of the user, taken more than a day before theabnormal medical event.
 19. The system of claim 18, wherein the abnormalmedical event is selected from a group comprising ear infection,cerebrovascular accident, and mastoiditis.
 20. A method for detecting anabnormal medical event, comprising: measuring, utilizing at least oneright-side head-mounted device, at least two signals indicative ofphotoplethysmographic signals (PPG_(SR1) and PPG_(SR2), respectively) atfirst and second regions of interest (ROI_(R1) and ROI_(R2),respectively) on the right side of a user's head; wherein ROI_(R1) andROI_(R2) are located at least 2 cm apart; measuring, utilizing at leastone left-side head-mounted device, at least two signals indicative ofphotoplethysmographic signals (PPG_(SL1) and PPG_(SL2), respectively) atfirst and second regions of interest (ROI_(L1) and ROI_(L2),respectively) on the left side of the user's head; wherein ROI_(L1) andROI_(L2) are located at least 2 cm apart; and detecting the abnormalmedical event based on an asymmetrical change to blood flow recognizablein PPG_(SR1), PPG_(SR2), PPG_(SL1), and PPG_(SL2), relative to abaseline based on previous measurements of PPG_(SR1), PPG_(SR2),PPG_(SL1), and PPG_(SL2) of the user, taken before the abnormal medicalevent.